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  • 1. Aslak, Ulf
    et al.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lehmann, Sune
    Constrained information flows in temporal networks reveal intermittent communities2018Ingår i: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 97, nr 6, artikel-id 062312Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Many real-world networks represent dynamic systems with interactions that change over time, often in uncoordinated ways and at irregular intervals. For example, university students connect in intermittent groups that repeatedly form and dissolve based on multiple factors, including their lectures, interests, and friends. Such dynamic systems can be represented as multilayer networkswhere each layer represents a snapshot of the temporal network. In this representation, it is crucial that the links between layers accurately capture real dependencies between those layers. Often, however, these dependencies are unknown. Therefore, current methods connect layers based on simplistic assumptions that do not capture node-level layer dependencies. For example, connecting every node to itself in other layers with the same weight can wipe out dependencies between intermittent groups, making it difficult or even impossible to identify them. In this paper, we present a principled approach to estimating node-level layer dependencies based on the network structure within each layer. We implement our node-level coupling method in the community detection framework Infomap and demonstrate its performance compared to current methods on synthetic and real temporal networks. We show that our approach more effectively constrains information inside multilayer communities so that Infomap can better recover planted groups in multilayer benchmark networks that represent multiple modeswith different groups and better identify intermittent communities in real temporal contact networks. These results suggest that node-level layer coupling can improve the modeling of information spreading in temporal networks and better capture intermittent community structure.

  • 2. Bae, Seung-Hee
    et al.
    Halperin, Daniel
    West, Jevin D.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Howe, Bill
    Scalable and Efficient Flow-Based Community Detection for Large-Scale Graph Analysis2017Ingår i: ACM Transactions on Knowledge Discovery from Data, ISSN 1556-4681, E-ISSN 1556-472X, Vol. 11, nr 3, artikel-id 32Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Community detection is an increasingly popular approach to uncover important structures in large networks. Flow-based community detection methods rely on communication patterns of the network rather than structural properties to determine communities. The Infomap algorithm in particular optimizes a novel objective function called the map equation and has been shown to outperform other approaches in third-party benchmarks. However, Infomap and its variants are inherently sequential, limiting their use for large-scale graphs. In this article, we propose a novel algorithm to optimize the map equation called RelaxMap. RelaxMap provides two important improvements over Infomap: parallelization, so that the map equation can be optimized over much larger graphs, and prioritization, so that the most important work occurs first, iterations take less time, and the algorithm converges faster. We implement these techniques using OpenMP on shared-memory multicore systems, and evaluate our approach on a variety of graphs from standard graph clustering benchmarks as well as real graph datasets. Our evaluation shows that both techniques are effective: RelaxMap achieves 70% parallel efficiency on eight cores, and prioritization improves algorithm performance by an additional 20-50% on average, depending on the graph properties. Additionally, RelaxMap converges in the similar number of iterations and provides solutions of equivalent quality as the serial Infomap implementation.

  • 3. Bae, Seung-Hee
    et al.
    Halperin, Daniel
    West, Jevin
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Howe, Bill
    Scalable Flow-Based Community Detection for Large-Scale Network Analysis2013Ingår i: 2013 IEEE 13TH INTERNATIONAL CONFERENCE ON DATA MINING WORKSHOPS (ICDMW) / [ed] Ding, W Washio, T Xiong, H Karypis, G Thuraisingham, B Cook, D Wu, X, IEEE, 2013, s. 303-310Konferensbidrag (Refereegranskat)
    Abstract [en]

    Community-detection is a powerful approach to uncover important structures in large networks. Since networks often describe flow of some entity, flow-based community-detection methods are particularly interesting. One such algorithm is called Infomap, which optimizes the objective function known as the map equation. While Infomap is known to be an effective algorithm, its serial implementation cannot take advantage of multicore processing in modern computers. In this paper, we propose a novel parallel generalization of Infomap called RelaxMap. This algorithm relaxes concurrency assumptions to avoid lock overhead, achieving 70% parallel efficiency in shared-memory multicore experiments while exhibiting similar convergence properties and finding similar community structures as the serial algorithm. We evaluate our approach on a variety of real graph datasets as well as synthetic graphs produced by a popular graph generator used for benchmarking community detection algorithms. We describe the algorithm, the experiments, and some emerging research directions in high-performance community detection on massive graphs.

  • 4.
    Bassolas, Aleix
    et al.
    School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom; Departament d'Enginyeria Informatica i Matematiques, Universitat Rovira i Virgili, Tarragona, Spain; Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain.
    Holmgren, Anton
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Marot, Antoine
    RTE Réseau de Transport d'Electricité, Paris, France.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Nicosia, Vincenzo
    School of Mathematical Sciences, Queen Mary University of London, London, United Kingdom.
    Mapping nonlocal relationships between metadata and network structure with metadata-dependent encoding of random walks2022Ingår i: Science Advances, E-ISSN 2375-2548, Vol. 8, nr 43, artikel-id eabn7558Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Integrating structural information and metadata, such as gender, social status, or interests, enriches networks and enables a better understanding of the large-scale structure of complex systems. However, existing approaches to augment networks with metadata for community detection only consider immediately adjacent nodes and cannot exploit the nonlocal relationships between metadata and large-scale network structure present in many spatial and social systems. Here, we develop a flow-based community detection framework based on the map equation that integrates network information and metadata of distant nodes and reveals more complex relationships. We analyze social and spatial networks and find that our methodology can detect functional metadata-informed communities distinct from those derived solely from network information or metadata. For example, in a mobility network of London, we identify communities that reflect the heterogeneity of income distribution, and in a European power grid network, we identify communities that capture relationships between geography and energy prices beyond country borders.

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  • 5. Bech, Morten L
    et al.
    Bergstrom, Carl T
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Garratt, Rodney J
    Mapping change in the overnight money market2015Ingår i: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 424, s. 44-51Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We use an information-theoretic approach to describe changes in lending relationships between financial institutions around the time of the Lehman Brothers failure. Unlike previous work that conducts maximum likelihood estimation on undirected networks our analysis distinguishes between borrowers and lenders and looks for broader lending relationships (multi-bank lending cycles) that extend beyond the immediate counter-parties. We detect significant changes in lending patterns following implementation of the Interest on Required and Excess Reserves policy by the Federal Reserve in October 2008. Analysis of micro-scale rates of change in the data suggests these changes were triggered by the collapse of Lehman Brothers a few weeks before.

  • 6.
    Bergstrom, Carl T
    et al.
    Department of Biology, University of Washington.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Response to commentaries on “The transmission sense of information”: discussion note2011Ingår i: Biology & Philosophy, ISSN 0169-3867, Vol. 26, nr 2, s. 195-200Artikel i tidskrift (Refereegranskat)
  • 7.
    Bergstrom, Carl T
    et al.
    Department of Biology, University of Washington.
    Rosvall, Martin
    Department of Biology, University of Washington.
    The transmission sense of information2011Ingår i: Biology & Philosophy, ISSN 0169-3867, E-ISSN 1572-8404, Vol. 26, nr 2, s. 159-176Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Biologists rely heavily on the language of information, coding, and transmission that is commonplace in the field of information theory developed by Claude Shannon, but there is open debate about whether such language is anything more than facile metaphor. Philosophers of biology have argued that when biologists talk about information in genes and in evolution, they are not talking about the sort of information that Shannon’s theory addresses. First, philosophers have suggested that Shannon’s theory is only useful for developing a shallow notion of correlation, the so-called “causal sense” of information. Second, they typically argue that in genetics and evolutionary biology, information language is used in a “semantic sense,” whereas semantics are deliberately omitted from Shannon’s theory. Neither critique is well-founded. Here we propose an alternative to the causal and semantic senses of information: atransmission sense of information, in which an object X conveys information if the function of X is to reduce, by virtue of its sequence properties, uncertainty on the part of an agent who observes X. The transmission sense not only captures much of what biologists intend when they talk about information in genes, but also brings Shannon’s theory back to the fore. By taking the viewpoint of a communications engineer and focusing on the decision problem of how information is to be packaged for transport, this approach resolves several problems that have plagued the information concept in biology, and highlights a number of important features of the way that information is encoded, stored, and transmitted as genetic sequence.

  • 8. Bernardo-Madrid, Rubén
    et al.
    Calatayud, Joaquín
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Department of Life Science, Universidad de Alcalá, Alcalá de Henares, Spain; Department of Biogeography and Global Change, Museo Nacional de Ciencias Naturales (MNCN-CSIC), Madrid, Spain.
    González-Suárez, Manuela
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lucas, Pablo M.
    Rueda, Marta
    Antonelli, Alexandre
    Revilla, Eloy
    Human activity is altering the world's zoogeographical regions2019Ingår i: Ecology Letters, ISSN 1461-023X, E-ISSN 1461-0248, Vol. 22, nr 8, s. 1297-1305Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Zoogeographical regions, or zooregions, are areas of the Earth defined by species pools that reflect ecological, historical and evolutionary processes acting over millions of years. Consequently, researchers have assumed that zooregions are robust and unlikely to change on a human timescale. However, the increasing number of human‐mediated introductions and extinctions can challenge this assumption. By delineating zooregions with a network‐based algorithm, here we show that introductions and extinctions are altering the zooregions we know today. Introductions are homogenising the Eurasian and African mammal zooregions and also triggering less intuitive effects in birds and amphibians, such as dividing and redefining zooregions representing the Old and New World. Furthermore, these Old and New World amphibian zooregions are no longer detected when considering introductions plus extinctions of the most threatened species. Our findings highlight the profound and far‐reaching impact of human activity and call for identifying and protecting the uniqueness of biotic assemblages.

  • 9.
    Blomberg, Jeanette
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Tasselius, Viktor
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Biostatistics, School of Public Health and Community Medicine, Gothenburg University, Gothenburg, Sweden.
    Vergara, Alexander
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Karamat, Fazeelat
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Imran, Qari Muhammad
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Strand, Åsa
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysiologisk botanik. Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Umeå Plant Science Centre (UPSC).
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Björklund, Stefan
    Umeå universitet, Medicinska fakulteten, Institutionen för medicinsk kemi och biofysik.
    Pseudomonas syringae infectivity correlates to altered transcript and metabolite levels of Arabidopsis mediator mutants2024Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 14, nr 1, artikel-id 6771Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Rapid metabolic responses to pathogens are essential for plant survival and depend on numerous transcription factors. Mediator is the major transcriptional co-regulator for integration and transmission of signals from transcriptional regulators to RNA polymerase II. Using four Arabidopsis Mediator mutants, med16, med18, med25 and cdk8, we studied how differences in regulation of their transcript and metabolite levels correlate to their responses to Pseudomonas syringae infection. We found that med16 and cdk8 were susceptible, while med25 showed increased resistance. Glucosinolate, phytoalexin and carbohydrate levels were reduced already before infection in med16 and cdk8, but increased in med25, which also displayed increased benzenoids levels. Early after infection, wild type plants showed reduced glucosinolate and nucleoside levels, but increases in amino acids, benzenoids, oxylipins and the phytoalexin camalexin. The Mediator mutants showed altered levels of these metabolites and in regulation of genes encoding key enzymes for their metabolism. At later stage, mutants displayed defective levels of specific amino acids, carbohydrates, lipids and jasmonates which correlated to their infection response phenotypes. Our results reveal that MED16, MED25 and CDK8 are required for a proper, coordinated transcriptional response of genes which encode enzymes involved in important metabolic pathways for Arabidopsis responses to Pseudomonas syringae infections.

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  • 10.
    Blöcker, Christopher
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Nieves, Juan Carlos
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Map equation centrality: community-aware centrality based on the map equation2022Ingår i: Applied Network Science, E-ISSN 2364-8228, Vol. 7, nr 1, artikel-id 56Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network's modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node's local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.

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  • 11.
    Blöcker, Christopher
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping flows on bipartite networks2020Ingår i: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 102, nr 5, artikel-id 052305Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Mapping network flows provides insight into the organization of networks, but even though many real networks are bipartite, no method for mapping flows takes advantage of the bipartite structure. What do we miss by discarding this information and how can we use it to understand the structure of bipartite networks better? The map equation models network flows with a random walk and exploits the information-theoretic duality between compression and finding regularities to detect communities in networks. However, it does not use the fact that random walks in bipartite networks alternate between node types, information worth 1 bit. To make some or all of this information available to the map equation, we developed a coding scheme that remembers node types at different rates. We explored the community landscape of bipartite real-world networks from no node-type information to full node-type information and found that using node types at a higher rate generally leads to deeper community hierarchies and a higher resolution. The corresponding compression of network flows exceeds the amount of extra information provided. Consequently, taking advantage of the bipartite structure increases the resolution and reveals more network regularities.

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  • 12.
    Blöcker, Christopher
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Smiljanic, Jelena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Scholtes, Ingo
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Similarity-based Link Prediction from Modular Compression of Network FlowsManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Node similarity scores constitute a foundation for machine learning in graphs. Besides clustering, node classification, and anomaly detection, they are a basis for link prediction with critical applications in biological systems, information networks, and recommender systems. Recent works on link prediction use vector space embeddings to calculate node similarities. While these methods can provide good performance in undirected networks, they have several disadvantages: limited interpretability, problem-specific hyperparameter tuning, manual model fitting through dimensionality reduction, and poor performance of symmetric similarities in directed link prediction. To address these issues, we propose MapSim, a novel information-theoretic approach to assess node similarities based on modular compression of network flows. Different from vector space embeddings, MapSim represents nodes in a discrete, non-metric space of communities and yields asymmetric similarities suitable to predict directed and undirected links in an unsupervised fashion. The resulting similarities can be explained based on a network's hierarchical modular organisation, facilitating interpretability. MapSim naturally accounts for Occam's razor, leading to parsimonious representations of clusters at multiple scales. Addressing unsupervised link prediction, we compare MapSim to popular embedding-based algorithms across 47 data sets of networks from a few hundred to hundreds of thousands of nodes and millions of links. Our analysis shows that MapSim's average performance across all networks is more than 7% higher than its closest competitor, outperforming all embedding methods in 14 of the 47 networks, and a more than 33% better worst-case performance. Our method demonstrates the potential of compression-based approaches in graph representation learning, with promising applications in other graph learning tasks.

  • 13.
    Blöcker, Christopher
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Smiljanic, Jelena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Scholtes, Ingo
    Center for Artificial Intelligence and Data Science, University of Würzburg, Germany.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Similarity-based link prediction from modular compression of network flows2022Ingår i: Proceedings of the First Learning on Graphs Conference, ML Research Press , 2022, s. 52:1-52:18Konferensbidrag (Refereegranskat)
    Abstract [en]

    Node similarity scores are a foundation for machine learning in graphs for clustering, node classification, anomaly detection, and link prediction with applications in biological systems, information networks, and recommender systems. Recent works on link prediction use vector space embeddings to calculate node similarities in undirected networks with good performance. Still, they have several disadvantages: limited interpretability, need for hyperparameter tuning, manual model fitting through dimensionality reduction, and poor performance from symmetric similarities in directed link prediction. We propose MapSim, an information-theoretic measure to assess node similarities based on modular compression of network flows. Unlike vector space embeddings, MapSim represents nodes in a discrete, non-metric space of communities and yields asymmetric similarities in an unsupervised fashion. We compare MapSim on a link prediction task to popular embedding-based algorithms across 47 networks and find that MapSim's average performance across all networks is more than 7% higher than its closest competitor, outperforming all embedding methods in 11 of the 47 networks. Our method demonstrates the potential of compression-based approaches in graph representation learning, with promising applications in other graph learning tasks.

  • 14.
    Bock Axelsen, Jacob
    et al.
    Niels Bohr Institute, Blegdansvej 17, DK 2100. Copenhagen, Denmark.
    Bernhardsson, Sebastian
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Sneppen, Kim
    Niels Bohr Institute, Blegdansvej 17, DK 2100. Copenhagen, Denmark.
    Trusina, Ala
    Niels Bohr Institute, Blegdansvej 17, DK 2100. Copenhagen, Denmark.
    Degree landscapes in scale-free networks2006Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics: Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics, ISSN 1063-651X, E-ISSN 1095-3787, Vol. 74, s. 036119-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We generalize the degree-organizational view of real-world networks with broad degree distributions in a landscape analog with mountains (high-degree nodes) and valleys (low-degree nodes). For example, correlated degrees between adjacent nodes correspond to smooth landscapes (social networks), hierarchical networks to one-mountain landscapes (the Internet), and degree-disassortative networks without hierarchical features to rough landscapes with several mountains. To quantify the topology, we here measure the widths of the mountains and the separation between different mountains. We also generate ridge landscapes to model networks organized under constraints imposed by the space the networks are embedded in, associated to spatial or in molecular networks to functional localization.

  • 15.
    Bohlin, Ludvig
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Edler, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lancichinetti, Andrea
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosval, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Community Detection and Visualization of Networks with the Map Equation Framework2014Ingår i: Measuring Scholarly Impact: Methods and Practice / [ed] Ying Ding, Ronald Rousseau, Dietmar Wolfram, Springer, 2014, s. 3-34Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    Large networks contain plentiful information about the organization of a system. The challenge is to extract useful information buried in the structure of myriad nodes and links. Therefore, powerful tools for simplifying and highlighting important structures in networks are essential for comprehending their organization. Such tools are called community-detection methods and they are designed to identify strongly intraconnected modules that often correspond to important functional units. Here we describe one such method, known as the map equation, and its accompanying algorithms for finding, evaluating, and visualizing the modular organization of networks. The map equation framework is very flexible and can identify two-level, multi-level, and overlapping organization in weighted, directed, and multiplex networks with its search algorithm Infomap. Because the map equation framework operates on the flow induced by the links of a network, it naturally captures flow of ideas and citation flow, and is therefore well-suited for analysis of bibliometric networks.

  • 16.
    Bohlin, Ludvig
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Stock Portfolio Structure of Individual Investors Infers Future Trading Behavior2014Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 9, nr 7, s. e103006-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Although the understanding of and motivation behind individual trading behavior is an important puzzle in finance, little is known about the connection between an investor's portfolio structure and her trading behavior in practice. In this paper, we investigate the relation between what stocks investors hold, and what stocks they buy, and show that investors with similar portfolio structures to a great extent trade in a similar way. With data from the central register of shareholdings in Sweden, we model the market in a similarity network, by considering investors as nodes, connected with links representing portfolio similarity. From the network, we find investor groups that not only identify different investment strategies, but also represent individual investors trading in a similar way. These findings suggest that the stock portfolios of investors hold meaningful information, which could be used to earn a better understanding of stock market dynamics.

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  • 17.
    Bohlin, Ludvig
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Viamontes Esquivel, Alcides
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lancichinetti, Andrea
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Robustness of journal rankings by network flows with different amounts of memory2016Ingår i: Journal of the Association for Information Science and Technology, ISSN 2330-1635, E-ISSN 2330-1643, Vol. 67, nr 10, s. 2527-2535Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    As the number of scientific journals has multiplied, journal rankings have become increasingly important for scientific decisions. From submissions and subscriptions to grants and hirings, researchers, policy makers, and funding agencies make important decisions influenced by journal rankings such as the ISI journal impact factor. Typically, the rankings are derived from the citation network between a selection of journals and unavoidably depend on this selection. However, little is known about how robust rankings are to the selection of included journals. We compare the robustness of three journal rankings based on network flows induced on citation networks. They model pathways of researchers navigating the scholarly literature, stepping between journals and remembering their previous steps to different degrees: zero-step memory as impact factor, one-step memory as Eigenfactor, and two-step memory, corresponding to zero-, first-, and second-order Markov models of citation flow between journals. We conclude that higher-order Markov models perform better and are more robust to the selection of journals. Whereas our analysis indicates that higher-order models perform better, the performance gain for higher-order Markov models comes at the cost of requiring more citation data over a longer time period.

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  • 18.
    Bóta, András
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Embedded Intelligent Systems Lab, Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, Luleå, Sweden.
    Holmberg, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Gardner, Lauren
    Department of Civil and Systems Engineering, Johns Hopkins University, MD, Baltimore, United States.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Socioeconomic and environmental patterns behind H1N1 spreading in Sweden2021Ingår i: Scientific Reports, E-ISSN 2045-2322, Vol. 11, nr 1, artikel-id 22512Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Identifying the critical factors related to influenza spreading is crucial in predicting and mitigating epidemics. Specifically, uncovering the relationship between epidemic onset and various risk indicators such as socioeconomic, mobility and climate factors can reveal locations and travel patterns that play critical roles in furthering an outbreak. We study the 2009 A(H1N1) influenza outbreaks in Sweden’s municipalities between 2009 and 2015 and use the Generalized Inverse Infection Method (GIIM) to assess the most significant contributing risk factors. GIIM represents an epidemic spreading process on a network: nodes correspond to geographical objects, links indicate travel routes, and transmission probabilities assigned to the links guide the infection process. Our results reinforce existing observations that the influenza outbreaks considered in this study were driven by the country’s largest population centers, while meteorological factors also contributed significantly. Travel and other socioeconomic indicators have a negligible effect. We also demonstrate that by training our model on the 2009 outbreak, we can predict the epidemic onsets in the following five seasons with high accuracy.

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  • 19.
    Calatayud, Joaquín
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Departamento de Biogeografía y Cambio Global, Museo Nacional de Ciencias Naturales, Madrid, Spain.
    Andivia, Enrique
    Escudero, Adrian
    Melian, Carlos J.
    Bernardo-Madrid, Ruben
    Stoffel, Markus
    Aponte, Cristina
    Medina, Nagore G.
    Molina-Venegas, Rafael
    Arnan, Xavier
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Neuman, Magnus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Ari Noriega, Jorge
    Alves-Martins, Fernanda
    Draper, Isabel
    Luzuriaga, Arantzazu
    Ballesteros-Canovas, Juan Antonio
    Morales-Molino, Cesar
    Ferrandis, Pablo
    Herrero, Asier
    Pataro, Luciano
    Juen, Leandro
    Cea, Alex
    Madrigal-Gonzalez, Jaime
    Positive associations among rare species and their persistence in ecological assemblages2020Ingår i: Nature Ecology & Evolution, E-ISSN 2397-334X, Vol. 4, nr 1, s. 40-45Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    According to the competitive exclusion principle, species with low competitive abilities should be excluded by more efficient competitors; yet, they generally remain as rare species. Here, we describe the positive and negative spatial association networks of 326 disparate assemblages, showing a general organization pattern that simultaneously supports the primacy of competition and the persistence of rare species. Abundant species monopolize negative associations in about 90% of the assemblages. On the other hand, rare species are mostly involved in positive associations, forming small network modules. Simulations suggest that positive interactions among rare species and microhabitat preferences are the most probable mechanisms underpinning this pattern and rare species persistence. The consistent results across taxa and geography suggest a general explanation for the maintenance of biodiversity in competitive environments. Analysing spatial association networks among >300 terrestrial and aquatic assemblages, the authors find that the majority of negative associations involve abundant species. In contrast, rare species form mostly positive associations, potentially explaining their persistence in natural communities.

  • 20.
    Calatayud, Joaquín
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Bernardo-Madrid, Ruben
    Neuman, Magnus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rojas, Alexis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Exploring the solution landscape enables more reliable network community detection2019Ingår i: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 100, nr 5, artikel-id 052308Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To understand how a complex system is organized and functions, researchers often identify communities in the system's network of interactions. Because it is practically impossible to explore all solutions to guarantee the best one, many community-detection algorithms rely on multiple stochastic searches. But for a given combination of network and stochastic algorithms, how many searches are sufficient to find a solution that is good enough? The standard approach is to pick a reasonably large number of searches and select the network partition with the highest quality or derive a consensus solution based on all network partitions. However, if different partitions have similar qualities such that the solution landscape is degenerate, the single best partition may miss relevant information, and a consensus solution may blur complementary communities. Here we address this degeneracy problem with coarse-grained descriptions of the solution landscape. We cluster network partitions based on their similarity and suggest an approach to determine the minimum number of searches required to describe the solution landscape adequately. To make good use of all partitions, we also propose different ways to explore the solution landscape, including a significance clustering procedure. We test these approaches on synthetic networks and a real-world network using two contrasting community-detection algorithms: The algorithm that can identify more general structures requires more searches, and networks with clearer community structures require fewer searches. We also find that exploring the coarse-grained solution landscape can reveal complementary solutions and enable more reliable community detection.

  • 21.
    Calatayud, Joaquín
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Departamento de Biología, Geología, Física y Química inorgánica, Universidad Rey Juan Carlos, Madrid, Spain.
    Neuman, Magnus
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rojas, Alexis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Eriksson, Anton
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Regularities in species’ niches reveal the world’s climate regions2021Ingår i: eLIFE, E-ISSN 2050-084X, Vol. 10, artikel-id e58397Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Climate regions form the basis of many ecological, evolutionary, and conservation studies. However, our understanding of climate regions is limited to how they shape vegetation: They do not account for the distribution of animals. Here we develop a network-based framework to identify important climates worldwide based on regularities in realized niches of about 26,000 tetrapods. We show that high-energy climates, including deserts, tropical savannas, and steppes, are consistent across animal-and plant-derived classifications, indicating similar underlying climatic determinants. Conversely, temperate climates differ across all groups, suggesting that these climates allow for idiosyncratic adaptations. Finally, we show how the integration of niche classifications with geographical information enables the detection of climatic transition zones and the signal of geographic and historical processes. Our results identify the climates shaping the distribution of tetrapods and call for caution when using general climate classifications to study the ecology, evolution, or conservation of specific taxa.

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  • 22. De Domenico, Manlio
    et al.
    Lancichinetti, Andrea
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Arenas, Alex
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Identifying Modular Flows on Multilayer Networks Reveals Highly Overlapping Organization in Interconnected Systems2015Ingår i: Physical Review X, E-ISSN 2160-3308, Vol. 5, nr 1, artikel-id 011027Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    To comprehend interconnected systems across the social and natural sciences, researchers have developed many powerful methods to identify functional modules. For example, with interaction data aggregated into a single network layer, flow-based methods have proven useful for identifying modular dynamics in weighted and directed networks that capture constraints on flow processes. However, many interconnected systems consist of agents or components that exhibit multiple layers of interactions, possibly from several different processes. Inevitably, representing this intricate network of networks as a single aggregated network leads to information loss and may obscure the actual organization. Here, we propose a method based on a compression of network flows that can identify modular flows both within and across layers in nonaggregated multilayer networks. Our numerical experiments on synthetic multilayer networks, with some layers originating from the same interaction process, show that the analysis fails in aggregated networks or when treating the layers separately, whereas the multilayer method can accurately identify modules across layers that originate from the same interaction process. We capitalize on our findings and reveal the community structure of two multilayer collaboration networks with topics as layers: scientists affiliated with the Pierre Auger Observatory and scientists publishing works on networks on the arXiv. Compared to conventional aggregated methods, the multilayer method uncovers connected topics and reveals smaller modules with more overlap that better capture the actual organization.

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  • 23.
    Derlén, Mattias
    et al.
    Umeå universitet, Samhällsvetenskapliga fakulteten, Juridiska institutionen.
    Lindholm, Johan
    Umeå universitet, Samhällsvetenskapliga fakulteten, Juridiska institutionen.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mirshahvalad, Atieh
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Coherence out of chaos: mapping European union law by running randomly through the maze of CJEU case law2013Ingår i: Europarättslig tidskrift, ISSN 1403-8722, E-ISSN 2002-3561, nr 3, s. 517-535Artikel i tidskrift (Refereegranskat)
    Abstract [no]

    Recent research has demonstrated the ability of network analysis to better understand law. In this study we apply network analysis to the case law of the European Court of Justice (CJEU) in order understand its role as a source of law. In doing so, we apply network analysis tools not previously used in legal scholarship, most significantly (i) a modified version of the PageRank algorithm, (ii) the Map Equation, and (iii) resampling to infer “missing” links. In the article we demonstrate that this method can help us to understand not only the CJEU’s case law but law generally.

  • 24.
    Edler, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Bohlin, Ludvig
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping Higher-Order Network Flows in Memory and Multilayer Networks with Infomap2017Ingår i: Algorithms, E-ISSN 1999-4893, Vol. 10, nr 4, artikel-id 112Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Comprehending complex systems by simplifying and highlighting important dynamical patterns requires modeling and mapping higher-order network flows. However, complex systems come in many forms and demand a range of representations, including memory and multilayer networks, which in turn call for versatile community-detection algorithms to reveal important modular regularities in the flows. Here we show that various forms of higher-order network flows can be represented in a unified way with networks that distinguish physical nodes for representing a complex system's objects from state nodes for describing flows between the objects. Moreover, these so-called sparse memory networks allow the information-theoretic community detection method known as the map equation to identify overlapping and nested flow modules in data from a range of different higher-order interactions such as multistep, multi-source, and temporal data. We derive the map equation applied to sparse memory networks and describe its search algorithm Infomap, which can exploit the flexibility of sparse memory networks. Together they provide a general solution to reveal overlapping modular patterns in higher-order flows through complex systems.

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  • 25.
    Edler, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Department of Biological and Environmental Sciences, University of Gothenburg, PO Box 461, SE-405 30 Gothenburg, Sweden.
    Guedes, Thais
    Zizka, Alexander
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Antonelli, Alexandre
    Infomap Bioregions: Interactive Mapping of Biogeographical Regions from Species Distributions2017Ingår i: Systematic Biology, ISSN 1063-5157, E-ISSN 1076-836X, Vol. 66, nr 2, s. 197-204Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Biogeographical regions (bioregions) reveal how different sets of species are spatially grouped and therefore are important units for conservation, historical biogeography, ecology, and evolution. Several methods have been developed to identify bioregions based on species distribution data rather than expert opinion. One approach successfully applies network theory to simplify and highlight the underlying structure in species distributions. However, this method lacks tools for simple and efficient analysis. Here, we present Infomap Bioregions, an interactive web application that inputs species distribution data and generates bioregion maps. Species distributions may be provided as georeferenced point occurrences or range maps, and can be of local, regional, or global scale. The application uses a novel adaptive resolution method to make best use of often incomplete species distribution data. The results can be downloaded as vector graphics, shapefiles, or in table format. We validate the tool by processing large data sets of publicly available species distribution data of the world's amphibians using species ranges, and mammals using point occurrences. We then calculate the fit between the inferred bioregions and WWF ecoregions. As examples of applications, researchers can reconstruct ancestral ranges in historical biogeography or identify indicator species for targeted conservation.

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  • 26.
    Edler, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden; Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden.
    Holmgren, Anton
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rojas, Alexis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Antonelli, Alexandre
    Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden; Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden; Department of Biology, University of Oxford, Oxford, United Kingdom; Royal Botanical Gardens Kew, Richmond, Surrey, United Kingdom.
    Infomap Bioregions 2: exploring the interplay between biogeography and evolution2022Manuskript (preprint) (Övrigt vetenskapligt)
  • 27.
    Edler, Daniel
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Smiljanić, Jelena
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Institute of Physics, University of Belgrade, Belgrade, Serbia.
    Holmgren, Anton
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Antonelli, Alexandre
    Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden; Gothenburg Global Biodiversity Centre, Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden; Department of Plant Sciences, University of Oxford, Oxford, United Kingdom; Royal Botanic Gardens, Kew, Richmond, Surrey, United Kingdom.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Variable Markov dynamics as a multifocal lens to map multiscale complex networksManuskript (preprint) (Övrigt vetenskapligt)
  • 28.
    Eriksson, Anton
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Carletti, Timoteo
    University of Namur, Namur, Belgium.
    Lambiotte, Renaud
    University of Oxford, Oxford, United Kingdom.
    Rojas, Alexis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Flow-Based Community Detection in Hypergraphs2022Ingår i: Higher-Order Systems / [ed] Federico Battiston; Giovanni Petri, Springer Science+Business Media B.V., 2022, , s. 21s. 141-161Kapitel i bok, del av antologi (Refereegranskat)
    Abstract [en]

    To connect structure, dynamics and function in systems with multibody interactions, network scientists model random walks on hypergraphs and identify communities that confine the walks for a long time. The two flow-based community-detection methods Markov stability and the map equation identify such communities based on different principles and search algorithms. But how similar are the resulting communities? We explain both methods’ machinery applied to hypergraphs and compare them on synthetic and real-world hypergraphs using various hyperedge-size biased random walks and time scales. We find that the map equation is more sensitive to time-scale changes and that Markov stability is more sensitive to hyperedge-size biases.

  • 29.
    Eriksson, Anton
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Edler, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rojas, Alexis
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    de Domenico, Manlio
    CoMuNe Lab, Fondazione Bruno Kessler, Povo (TN), Italy.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    How choosing random-walk model and network representation matters for flow-based community detection in hypergraphs2021Ingår i: Communications Physics, E-ISSN 2399-3650, Vol. 4, nr 1, artikel-id 133Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Hypergraphs offer an explicit formalism to describe multibody interactions in complex systems. To connect dynamics and function in systems with these higher-order interactions, network scientists have generalised random-walk models to hypergraphs and studied the multibody effects on flow-based centrality measures. Mapping the large-scale structure of those flows requires effective community detection methods applied to cogent network representations. For different hypergraph data and research questions, which combination of random-walk model and network representation is best? We define unipartite, bipartite, and multilayer network representations of hypergraph flows and explore how they and the underlying random-walk model change the number, size, depth, and overlap of identified multilevel communities. These results help researchers choose the appropriate modelling approach when mapping flows on hypergraphs.

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  • 30.
    Fahlman, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Hellström, G.
    Jonsson, Micael
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Fick, J.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Klaminder, Jonatan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Behavior of shoaling fish populations is not responsive to anxiolytics after habituation to lake conditionsManuskript (preprint) (Övrigt vetenskapligt)
  • 31.
    Fahlman, Johan
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Jonsson, Micael
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Berglund Fick, Jerker
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Kemiska institutionen.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Klaminder, Jonatan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Impacts of Oxazepam on Perch (Perca fluviatilis) Behavior: Fish Familiarized to Lake Conditions Do Not Show Predicted Anti-anxiety Response2021Ingår i: Environmental Science and Technology, ISSN 0013-936X, E-ISSN 1520-5851, Vol. 55, nr 6, s. 3624-3633Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A current theory in environmental science states that dissolved anxiolytics (oxazepam) from wastewater effluents can reduce anti-predator behavior in fish with potentially negative impacts on prey fish populations. Here, we hypothesize that European perch (Perca fluviatilis) populations being exposed to oxazepam in situ show reduced anti-predator behavior, which has previously been observed for exposed isolated fish in laboratory studies. We tested our hypothesis by exposing a whole-lake ecosystem, containing both perch (prey) and northern pike (Esox lucius; predator), to oxazepam while tracking fish behavior before and after exposure in the exposed lake as well as in an unexposed nearby lake (control). Oxazepam concentrations in the exposed lake ranged between 11 and 24 μg L-1, which is >200 times higher than concentrations reported for European rivers. In contrast to our hypothesis, we did not observe an oxazepam-induced reduction in anti-predator behavior, inferred from perch swimming activity, distance to predators, distance to conspecifics, home-range size, and habitat use. In fact, exposure to oxazepam instead stimulated anti-predator behavior (decreased activity, decreased distance to conspecifics, and increased littoral habitat use) when using behavior in the control lake as a reference. Shoal dynamics and temperature changes may have masked modest reductions in anti-predator behavior due to oxazepam. Although we cannot fully resolve the mechanism(s) behind our observations, our results indicate that the effects of oxazepam on perch behavior in a familiar natural ecosystem are negligible in comparison to the effects of other environmental conditions.

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  • 32.
    Fernández, Leyden
    et al.
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Integrated Science Lab (Icelab), Umeå University, Umeå, Sweden.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Integrated Science Lab (Icelab), Umeå University, Umeå, Sweden.
    Normark, Johan
    Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för klinisk mikrobiologi.
    Fällman, Maria
    Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Molekylär Infektionsmedicin, Sverige (MIMS). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Integrated Science Lab (Icelab), Umeå University, Umeå, Sweden.
    Avican, Kemal
    Umeå universitet, Medicinska fakulteten, Umeå Centre for Microbial Research (UCMR). Umeå universitet, Medicinska fakulteten, Institutionen för molekylärbiologi (Medicinska fakulteten). Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Integrated Science Lab (Icelab), Umeå University, Umeå, Sweden.
    Co-PATHOgenex web application for assessing complex stress responses in pathogenic bacteria2024Ingår i: Microbiology Spectrum, E-ISSN 2165-0497, Vol. 12, nr 1, artikel-id e02781-23Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Pathogenic bacteria encounter various stressors while residing in the host. They respond through intricate mechanisms of gene expression regulation, ensuring their survival and adaptation. Understanding how bacteria adapt to different stress conditions through regulatory processes of specific genes requires exploring complex transcriptional responses using gene co-expression networks. We employed a large transcriptome data set comprising 32 diverse human bacterial pathogens exposed to the same 11 host-mimicking stress conditions. Using the weighted gene co-expression network analysis algorithm, we generated bacterial gene co-expression networks. By associating modular eigengene expression with specific stress conditions, we identified gene co-expression modules and stress-specific stimulons, including genes with unique expression patterns under specific stress conditions. Suggesting a new potential role of the frm operon in responding to bile stress in enteropathogenic bacteria demonstrates the effectiveness of our approach. We also revealed the regulation of streptolysin S genes, involved in the production, processing, and export of streptolysin S, a toxin responsible for the beta-hemolytic phenotype of group A Streptococcus. In a comparative analysis of stress responses in three Escherichia coli strains from the core transcriptome, we revealed shared and unique expression patterns across the strains, offering insights into convergent and divergent stress responses. To help researchers perform similar analyses, we created the user-friendly web application Co-PATHOgenex. This tool aids in deepening our understanding of bacterial adaptation to stress conditions and in deciphering complex transcriptional responses of bacterial pathogens.IMPORTANCEUnveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition. Unveiling gene co-expression networks in bacterial pathogens has the potential for gaining insights into their adaptive strategies within the host environment. Here, we developed Co-PATHOgenex, an interactive and user-friendly web application that enables users to construct networks from gene co-expressions using custom-defined thresholds (https://avicanlab.shinyapps.io/copathogenex/). The incorporated search functions and visualizations within the tool simplify the usage and facilitate the interpretation of the analysis output. Co-PATHOgenex also includes stress stimulons for various bacterial species, which can help identify gene products not previously associated with a particular stress condition.

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  • 33. Haring, Robin
    et al.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Völker, Uwe
    Völzke, Henry
    Kroemer, Heyo
    Nauck, Matthias
    Wallaschofski, Henri
    A Network-Based Approach to Visualize Prevalence and Progression of Metabolic Syndrome Components2012Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 7, nr 6, s. e39461-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: The additional clinical value of clustering cardiovascular risk factors to define the metabolic syndrome (MetS) is still under debate. However, it is unclear which cardiovascular risk factors tend to cluster predominately and how individual risk factor states change over time. Methods & Results: We used data from 3,187 individuals aged 20-79 years from the population-based Study of Health in Pomerania for a network-based approach to visualize clustered MetS risk factor states and their change over a five-year follow-up period. MetS was defined by harmonized Adult Treatment Panel III criteria, and each individual's risk factor burden was classified according to the five MetS components at baseline and follow-up. We used the map generator to depict 32 (2(5)) different states and highlight the most important transitions between the 1,024 (32(2)) possible states in the weighted directed network. At baseline, we found the largest fraction (19.3%) of all individuals free of any MetS risk factors and identified hypertension (15.4%) and central obesity (6.3%), as well as their combination (19.0%), as the most common MetS risk factors. Analyzing risk factor flow over the five-year follow-up, we found that most individuals remained in their risk factor state and that low high-density lipoprotein cholesterol (HDL) (6.3%) was the most prominent additional risk factor beyond hypertension and central obesity. Also among individuals without any MetS risk factor at baseline, low HDL (3.5%), hypertension (2.1%), and central obesity (1.6%) were the first risk factors to manifest during follow-up. Conclusions: We identified hypertension and central obesity as the predominant MetS risk factor cluster and low HDL concentrations as the most prominent new onset risk factor.

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  • 34.
    Holme, Petter
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Komplexa system och nätverksfysik: Om hur saker hänger ihop ger andra saker deras egenskaper2009Ingår i: Kosmos, Fysikersamfundet , 2009Kapitel i bok, del av antologi (Övrig (populärvetenskap, debatt, mm))
    Abstract [sv]

    Många system i samhället och naturen − från börskurser, ryktesspridning och etniska konflikter till snöflingor, epidemier och fågelflockar − får sina egenskaper av växelverkan mellan ett stort antal delar. Hur saker hänger ihop gör skillnad och forsk- ning om komplexa system handlar om att förstå hur makrosko- piska fenomen uppkommer från mikroskopiska interaktioner. Här gör vi några nedslag i detta tvärvetenskapliga område som har sin matematiska grund i statistisk fysik.

  • 35.
    Holmgren, Anton
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Blöcker, Christopher
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping biased higher-order walks reveals overlapping communitiesManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    Researchers use networks to model relational data from complex systems, and tools from network science to map them and understand their function. Flow communities capture the organization of various real-world systems such as social networks, protein-protein interactions, and species distributions, and are often overlapping. However, mapping overlapping flow-based communities requires higher-order data, which is not always available. To address this issue, we take inspiration from the representation-learning algorithm node2vec, and apply higher-order biased random walks on first-order networks to obtain higher-order data. But instead of explicitly simulating the walks, we model them with sparse memory networks and control the complexity of the higher-order model with an information-theoretic approach through a tunable information-loss parameter. Using the map equation framework, we partition the resulting higher-order networks into overlapping modules. We find that our method recovers planted overlapping partitions in synthetic benchmarks and identifies overlapping communities in real-world networks.

  • 36.
    Holmgren, Anton
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Edler, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping change in higher-order networks with multilevel and overlapping communitiesManuskript (preprint) (Övrigt vetenskapligt)
    Abstract [en]

    New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial generator to illustrate the effect of modeling network flows with memory in a citation network, distinguishing multidisciplinary from field-specific journals. 

  • 37.
    Holmgren, Anton
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Edler, Daniel
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Department of Biological and Environmental Sciences, Gothenburg Global Biodiversity Centre, University of Gothenburg, Gothenburg, Sweden.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping change in higher-order networks with multilevel and overlapping communities2023Ingår i: Applied Network Science, E-ISSN 2364-8228, Vol. 8, nr 1, artikel-id 42Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    New network models of complex systems use layers, state nodes, or hyperedges to capture higher-order interactions and dynamics. Simplifying how the higher-order networks change over time or depending on the network model would be easy with alluvial diagrams, which visualize community splits and merges between networks. However, alluvial diagrams were developed for networks with regular nodes assigned to non-overlapping flat communities. How should they be defined for nodes in layers, state nodes, or hyperedges? How can they depict multilevel, overlapping communities? Here we generalize alluvial diagrams to map change in higher-order networks and provide an interactive tool for anyone to generate alluvial diagrams. We use the alluvial diagram generator in three case studies to illustrate significant changes in the organization of science, the effect of modeling network flows with memory in a citation network and distinguishing multidisciplinary from field-specific journals, and the effects of multilayer representation of a collaboration hypergraph.

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  • 38.
    Hotchkiss, E. R.
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Hall, R. O., Jr.
    Sponseller, R. A.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Butman, D.
    Klaminder, J.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Laudon, H.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Karlsson, J.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för ekologi, miljö och geovetenskap.
    Sources of and processes controlling CO2 emissions change with the size of streams and rivers2015Ingår i: Nature Geoscience, ISSN 1752-0894, E-ISSN 1752-0908, Vol. 8, nr 9, s. 696-699Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Carbon dioxide (CO2) evasion from streams and rivers to the atmosphere represents a substantial flux in the global carbon cycle(1-3). The proportions of CO2 emitted from streams and rivers that come from terrestrially derived CO2 or from CO2 produced within freshwater ecosystems through aquatic metabolism are not well quantified. Here we estimated CO2 emissions from running waters in the contiguous United States, based on freshwater chemical and physical characteristics and modelled gas transfer velocities at 1463 United States Geological Survey monitoring sites. We then assessed CO2 production from aquatic metabolism, compiled from previously published measurements of net ecosystem production from 187 streams and rivers across the contiguous United States. We find that CO2 produced by aquatic metabolism contributes about 28% of CO2 evasion from streams and rivers with flows between 0.0001 and 19,000 m(3) s(-1). We mathematically modelled CO2 flux from groundwater into running waters along a stream-river continuum to evaluate the relationship between stream size and CO2 source. Terrestrially derived CO2 dominates emissions from small streams, and the percentage of CO2 emissions from aquatic metabolism increases with stream size. We suggest that the relative role of rivers as conduits for terrestrial CO2 efflux and as reactors mineralizing terrestrial organic carbon is a function of their size and connectivity with landscapes.

  • 39.
    Karimi, Fariba
    et al.
    Leibniz Institute for the Social Sciences, Cologne, Germany.
    Bohlin, Ludvig
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Samoilenko, Ann
    Leibniz-Institute for the Social Sciences, Cologne, Germany.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lancichinetti, Andrea
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Mapping bilateral information interests using the activity of Wikipedia editors2015Ingår i: Palgrave communications, ISSN 2055-1045, Vol. 1, s. 1-7, artikel-id 15041Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We live in a global village where electronic communication has eliminated the geographical barriers of information exchange. The road is now open to worldwide convergence of information interests, shared values and understanding. Nevertheless, interests still vary between countries around the world. This raises important questions about what today’s world map of information interests actually looks like and what factors cause the barriers of information exchange between countries. To quantitatively construct a world map of information interests, we devise a scalable statistical model that identifies countries with similar information interests and measures the countries’ bilateral similarities. From the similarities we connect countries in a global network and find that countries can be mapped into 18 clusters with similar information interests. Through regression we find that language and religion best explain the strength of the bilateral ties and formation of clusters. Our findings provide a quantitative basis for further studies to better understand the complex interplay between shared interests and conflict on a global scale. The methodology can also be extended to track changes over time and capture important trends in global information exchange.

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  • 40. Kawamoto, Tatsuro
    et al.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Estimating the resolution limit of the map equation in community detection2015Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, nr 1, s. 012809-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A community detection algorithm is considered to have a resolution limit if the scale of the smallest modules that can be resolved depends on the size of the analyzed subnetwork. The resolution limit is known to prevent some community detection algorithms from accurately identifying the modular structure of a network. In fact, any global objective function for measuring the quality of a two-level assignment of nodes into modules must have some sort of resolution limit or an external resolution parameter. However, it is yet unknown how the resolution limit affects the so-called map equation, which is known to be an efficient objective function for community detection. We derive an analytical estimate and conclude that the resolution limit of the map equation is set by the total number of links between modules instead of the total number of links in the full network as for modularity. This mechanism makes the resolution limit much less restrictive for the map equation than for modularity; in practice, it is orders of magnitudes smaller. Furthermore, we argue that the effect of the resolution limit often results from shoehorning multilevel modular structures into two-level descriptions. As we show, the hierarchical map equation effectively eliminates the resolution limit for networks with nested multilevel modular structures.

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  • 41.
    Kheirkhahzadeh, Masoumeh
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik. Department of IT and Computer Engineering, Iran University of Science and Technology, Teheran, Iran.
    Lancichinetti, Andrea
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Efficient community detection of network flows for varying Markov times and bipartite networks2016Ingår i: Physical Review E, ISSN 2470-0045, Vol. 93, nr 3, artikel-id 032309Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Community detection of network flows conventionally assumes one-step dynamics on the links. For sparse networks and interest in large-scale structures, longer timescales may be more appropriate. Oppositely, for large networks and interest in small-scale structures, shorter timescales may be better. However, current methods for analyzing networks at different timescales require expensive and often infeasible network reconstructions. To overcome this problem, we introduce a method that takes advantage of the inner workings of the map equation and evades the reconstruction step. This makes it possible to efficiently analyze large networks at different Markov times with no extra overhead cost. The method also evades the costly unipartite projection for identifying flow modules in bipartite networks.

  • 42.
    Kirkley, Alec
    et al.
    Institute of Data Science, University of Hong Kong, Hong Kong; Department of Urban Planning and Design, University of Hong Kong, Hong Kong; Urban Systems Institute, University of Hong Kong, Hong Kong.
    Rojas, Alexis
    Department of Computer Science, University of Helsinki, Helsinki, Finland.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Young, Jean-Gabriel
    Department of Mathematics and Statistics, University of Vermont, VT, Burlington, United States; Vermont Complex Systems Center, University of Vermont, VT, Burlington, United States.
    Compressing network populations with modal networks reveal structural diversity2023Ingår i: Communications Physics, E-ISSN 2399-3650, Vol. 6, nr 1, artikel-id 148Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Analyzing relational data consisting of multiple samples or layers involves critical challenges: How many networks are required to capture the variety of structures in the data? And what are the structures of these representative networks? We describe efficient nonparametric methods derived from the minimum description length principle to construct the network representations automatically. The methods input a population of networks or a multilayer network measured on a fixed set of nodes and output a small set of representative networks together with an assignment of each network sample or layer to one of the representative networks. We identify the representative networks and assign network samples to them with an efficient Monte Carlo scheme that minimizes our description length objective. For temporally ordered networks, we use a polynomial time dynamic programming approach that restricts the clusters of network layers to be temporally contiguous. These methods recover planted heterogeneity in synthetic network populations and identify essential structural heterogeneities in global trade and fossil record networks. Our methods are principled, scalable, parameter-free, and accommodate a wide range of data, providing a unified lens for exploratory analyses and preprocessing large sets of network samples.

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  • 43.
    Lambiotte, R.
    et al.
    Univ Namur, Dept Math & Naxys, B-5000 Namur, Belgium .
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Ranking and clustering of nodes in networks with smart teleportation2012Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 85, nr 5, s. 056107-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Random teleportation is a necessary evil for ranking and clustering directed networks based on random walks. Teleportation enables ergodic solutions, but the solutions must necessarily depend on the exact implementation and parametrization of the teleportation. For example, in the commonly used PageRank algorithm, the teleportation rate must trade off a heavily biased solution with a uniform solution. Here we show that teleportation to links rather than nodes enables a much smoother trade-off and effectively more robust results. We also show that, by not recording the teleportation steps of the random walker, we can further reduce the effect of teleportation with dramatic effects on clustering.

  • 44. Lambiotte, Renaud
    et al.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Scholtes, Ingo
    From networks to optimal higher-order models of complex systems2019Ingår i: Nature Physics, ISSN 1745-2473, E-ISSN 1745-2481, Vol. 15, nr 4, s. 313-320Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.

  • 45. Lambiotte, Renaud
    et al.
    Salnikov, Vsevolod
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Effect of memory on the dynamics of random walks on networks2015Ingår i: Journal of Complex Networks, ISSN 2051-1310, E-ISSN 2051-1329, Vol. 3, nr 2, s. 177-188Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Pathways of diffusion observed in real-world systems often require stochastic processes going beyond first-order Markov models, as implicitly assumed in network theory. In this work, we focus on second-order Markov models, and derive an analytical expression for the effect of memory on the spectral gap and thus, equivalently, on the characteristic time needed for the stochastic process to asymptotically reach equilibrium. Perturbation analysis shows that standard first-order Markov models can either overestimate or underestimate the diffusion rate of flows across the modular structure of a system captured by a second-order Markov network. We test the theoretical predictions on a toy example and on numerical data, and discuss their implications for network theory, in particular in the case of temporal or multiplex networks.

  • 46.
    Lizana, Ludvig
    et al.
    Niels Bohr Institute, Blegdamsvej 17, DK-2100, Copenhagen, Denmark.
    Rosvall, Martin
    Umeå universitet. Niels Bohr Institute, Blegdamsvej 17, DK-2100, Copenhagen, Denmark.
    Sneppen, Kim
    Niels Bohr Institute.
    Time walkers and spatial dynamics of aging information2010Ingår i: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 104, nr 4, artikel-id 040603Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The distribution of information is essential for a living system’s ability to coordinate and adapt. Random walkers are often used to model this distribution process and, in doing so, one effectively assumes that information maintains its relevance over time. But the value of information in social and biological systems often decays and must continuously be updated. To capture the spatial dynamics of aging information, we introduce time walkers. A time walker moves like a random walker, but interacts with traces left by other walkers, some representing older information, some newer. The traces form a navigable information landscape which we visualize as a river network. We quantify the dynamical properties of time walkers, and the quality of the information left behind, on a two-dimensional lattice. We show that searching in this landscape is superior to random searching.

  • 47.
    Minnhagen, Petter
    et al.
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskaplig fakultet, Fysik.
    Sneppen, Kim
    NORDITA, Blegdamsvej 17, DK 2100, Copenhagen, Denmark.
    Trusina, Ala
    Self-organization of structures and networks from merging and small-scale fluctuations2004Ingår i: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 340, nr 4, s. 725-732Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We discuss merging-and-creation as a self-organizing process for scale-free topologies in networks. Three power-law classes characterized by the power-law exponents 23 , 2 and 25 are identified and the process is generalized to networks. In the network context the merging can be viewed as a consequence of optimization related to more efficient signaling.

  • 48.
    Mirshahvalad, Atieh
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Beauchesne, Olivier
    Science-Metrix.
    Archambault, Éric
    Science-Metrix.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Resampling effects on significance analysis of network clustering and ranking2013Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 8, nr 1, artikel-id e53943Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Community detection helps us simplify the complex configuration of networks, but communities are reliable only if they are statistically significant. To detect statistically significant communities, a common approach is to resample the original network and analyze the communities. But resampling assumes independence between samples, while the components of a network are inherently dependent. Therefore, we must understand how breaking dependencies between resampled components affects the results of the significance analysis. Here we use scientific communication as a model system to analyze this effect. Our dataset includes citations among articles published in journals in the years 1984–2010. We compare parametric resampling of citations with non-parametric article resampling. While citation resampling breaks link dependencies, article resampling maintains such dependencies. We find that citation resampling underestimates the variance of link weights. Moreover, this underestimation explains most of the differences in the significance analysis of ranking and clustering. Therefore, when only link weights are available and article resampling is not an option, we suggest a simple parametric resampling scheme that generates link-weight variances close to the link-weight variances of article resampling. Nevertheless, when we highlight and summarize important structural changes in science, the more dependencies we can maintain in the resampling scheme, the earlier we can predict structural change. 

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  • 49.
    Mirshahvalad, Atieh
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Lindholm, Johan
    Umeå universitet, Samhällsvetenskapliga fakulteten, Juridiska institutionen.
    Derlén, Mattias
    Umeå universitet, Samhällsvetenskapliga fakulteten, Juridiska institutionen.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Significant Communities in Large Sparse Networks2012Ingår i: PLOS ONE, E-ISSN 1932-6203, Vol. 7, nr 3, s. e33721-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detected structure, one approach is to perturb the network structure by removing links and measure how much the communities change. However, perturbing sparse networks is challenging because they are inherently sensitive; they shatter easily if links are removed. Here we propose a simple method to perturb sparse networks and assess the significance of their communities. We generate resampled networks by adding extra links based on local information, then we aggregate the information from multiple resampled networks to find a coarse-grained description of significant clusters. In addition to testing our method on benchmark networks, we use our method on the sparse network of the European Court of Justice (ECJ) case law, to detect significant and insignificant areas of law. We use our significance analysis to draw a map of the ECJ case law network that reveals the relations between the areas of law.

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  • 50.
    Mirshahvalad, Atieh
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Rosvall, Martin
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för fysik.
    Reinforced communication and social navigation: Remember your friends and remember yourself2011Ingår i: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 84, nr 3, s. 036102-Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples' ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

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