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  • 1. Barrenas, Fredrik
    et al.
    Chavali, Sreenivas
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Mobini, Reza
    Benson, Mikael
    Network properties of complex human disease genes identified through genome-wide association studies2009In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 4, no 11, p. e8090-Article in journal (Refereed)
    Abstract [en]

    Background Previous studies of network properties of human disease genes have mainly focused on monogenic diseases or cancers and have suffered from discovery bias. Here we investigated the network properties of complex disease genes identified by genome-wide association studies (GWAs), thereby eliminating discovery bias.

    Principal findings We derived a network of complex diseases (n = 54) and complex disease genes (n = 349) to explore the shared genetic architecture of complex diseases. We evaluated the centrality measures of complex disease genes in comparison with essential and monogenic disease genes in the human interactome. The complex disease network showed that diseases belonging to the same disease class do not always share common disease genes. A possible explanation could be that the variants with higher minor allele frequency and larger effect size identified using GWAs constitute disjoint parts of the allelic spectra of similar complex diseases. The complex disease gene network showed high modularity with the size of the largest component being smaller than expected from a randomized null-model. This is consistent with limited sharing of genes between diseases. Complex disease genes are less central than the essential and monogenic disease genes in the human interactome. Genes associated with the same disease, compared to genes associated with different diseases, more often tend to share a protein-protein interaction and a Gene Ontology Biological Process.

    Conclusions This indicates that network neighbors of known disease genes form an important class of candidates for identifying novel genes for the same disease.

  • 2.
    Grönlund, Andreas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    A network-based threshold model for the spreading of fads in society and markets2005In: Advances in Complex Systems, ISSN 0219-5259, Vol. 8, no 2-3, p. 261-273Article in journal (Refereed)
    Abstract [en]

    We investigate the behavior of a threshold model for the spreading of fads and similar phenomena in society. The model is giving the fad dynamics and is intended to be confined to an underlying network structure. We investigate the whole parameter space of the fad dynamics on three types of network models. The dynamics we discover is rich and highly dependent on the underlying network structure. For some range of the parameter space, for all types of substrate networks, there are a great variety of sizes and life-lengths of the fads -- what one see in real-world social and economical systems.

  • 3.
    Grönlund, Andreas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Networking the seceder model: group formation in social and economic systems2004In: 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. 70, no 3, p. 036108-Article in journal (Refereed)
    Abstract [en]

    The seceder model illustrates how the desire to be different from the average can lead to formation of groups in a population. We turn the original, agent based, seceder model into a model of network evolution. We find that the structural characteristics of our model closely match empirical social networks. Statistics for the dynamics of group formation are also given. Extensions of the model to networks of companies are also discussed.

  • 4.
    Grönlund, Andreas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Plant Physiology. Umeå University, Faculty of Science and Technology, Umeå Plant Science Centre (UPSC).
    Holme, Petter
    Minnhagen, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Dynamic scaling regimes of collective decision making2008In: EPL - Europhysics Letters, Vol. 81, no 2, p. 28003-Article in journal (Refereed)
    Abstract [en]

    We investigate a social system of agents faced with a binary choice. We assume there is a correct, or beneficial, outcome of this choice. Furthermore, we assume agents are influenced by others in making their decision, and that the agents can obtain information that may guide them towards making a correct decision. The dynamic model we propose is of nonequilibrium type, converging to a final decision. We run it on random graphs and scale-free networks. On random graphs, we find two distinct regions in terms of the finalizing time —the time until all agents have finalized their decisions. On scale-free networks, on the other hand, there do not seem to be such distinct scaling regions.

  • 5.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University Suwon, South Korea.
    Analyzing temporal networks in social media2014In: Proceedings of the IEEE, ISSN 0018-9219, E-ISSN 1558-2256, Vol. 102, no 12, p. 1922-1933Article, review/survey (Refereed)
    Abstract [en]

    Many types of social media metadata come in forms of temporal networks, networks where we have information about not only who is in contact with whom but also when contacts happen. In this paper, we review methods to analyze temporal networks developed in the last few years applied to social media data. These methods seek to identify important spreaders and, in more generality, how the temporal and topological structure of interaction affects spreading processes.

  • 6.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, Suwon, Korea ; Department of Sociology, Stockholm University, Stockholm, Sweden.
    Epidemiologically optimal static networks from temporal network data2013In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 9, no 7, p. e1003142-Article in journal (Refereed)
    Abstract [en]

    One of network epidemiology's central assumptions is that the contact structure over which infectious diseases propagate can be represented as a static network. However, contacts are highly dynamic, changing at many time scales. In this paper, we investigate conceptually simple methods to construct static graphs for network epidemiology from temporal contact data. We evaluate these methods on empirical and synthetic model data. For almost all our cases, the network representation that captures most relevant information is a so-called exponential-threshold network. In these, each contact contributes with a weight decreasing exponentially with time, and there is an edge between a pair of vertices if the weight between them exceeds a threshold. Networks of aggregated contacts over an optimally chosen time window perform almost as good as the exponential-threshold networks. On the other hand, networks of accumulated contacts over the entire sampling time, and networks of concurrent partnerships, perform worse. We discuss these observations in the context of the temporal and topological structure of the data sets.

  • 7.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Sungkyunkwan University, Suwon, Korea and tockholm University, Stockholm, Sweden.
    Extinction Times of Epidemic Outbreaks in Networks2013In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 12, article id e84429Article in journal (Refereed)
    Abstract [en]

    In the Susceptible–Infectious–Recovered (SIR) model of disease spreading, the time to extinction of the epidemics happens at an intermediate value of the per-contact transmission probability. Too contagious infections burn out fast in the population. Infections that are not contagious enough die out before they spread to a large fraction of people. We characterize how the maximal extinction time in SIR simulations on networks depend on the network structure. For example we find that the average distances in isolated components, weighted by the component size, is a good predictor of the maximal time to extinction. Furthermore, the transmission probability giving the longest outbreaks is larger than, but otherwise seemingly independent of, the epidemic threshold.

  • 8.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Form and function of complex networks2004Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Networks are all around us, all the time. From the biochemistry of our cells to the web of friendships across the planet. From the circuitry of modern electronics to chains of historical events. A network is the result of the forces that shaped it. Thus the principles of network formation can be, to some extent, deciphered from the network itself. All such information comprises the structure of the network. The study of network structure is the core of modern network science. This thesis centres around three aspects of network structure: What kinds of network structures are there and how can they be measured? How can we build models for network formation that give the structure of networks in the real world? How does the network structure affect dynamical systems confined to the networks? These questions are discussed using a variety of statistical, analytical and modelling techniques developed by physicists, mathematicians, biologists, chemists, psychologists, sociologists and anthropologists. My own research touches all three questions. In this thesis I present works trying to answer: What is the best way to protect a network against sinister attacks? How do groups form in friendship networks? Where do traffic jams appear in a communication network? How is cellular metabolism organised? How do Swedes flirt on the Internet? . . . and many other questions.

  • 9.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Hur står det till med dina dynamiska nätverk?2011In: Thule: Kungl. Skytteanska Samfundets Årsbok 2011 / [ed] Roger Jacobsson, Umeå: Kungl. Skytteanska Samfundet , 2011, p. 111-121Chapter in book (Other (popular science, discussion, etc.))
  • 10.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Metabolic robustness and network modularity: a model study2011In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 6, no 2, p. e16605-Article in journal (Refereed)
    Abstract [en]

    Background Several studies have mentioned network modularity—that a network can easily be decomposed into subgraphs that are densely connected within and weakly connected between each other—as a factor affecting metabolic robustness. In this paper we measure the relation between network modularity and several aspects of robustness directly in a model system of metabolism.

    Methodology/Principal Findings By using a model for generating chemical reaction systems where one can tune the network modularity, we find that robustness increases with modularity for changes in the concentrations of metabolites, whereas it decreases with changes in the expression of enzymes. The same modularity scaling is true for the speed of relaxation after the perturbations.

    Conclusions/Significance Modularity is not a general principle for making metabolism either more or less robust; this question needs to be addressed specifically for different types of perturbations of the system.

  • 11.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Model validation of simple-graph representations of metabolism2009In: Journal of the Royal Society Interface, ISSN 1742-5689, E-ISSN 1742-5662, Vol. 6, no 40, p. 1027-1034Article in journal (Refereed)
    Abstract [en]

    The large-scale properties of chemical reaction systems, such as metabolism, can be studied with graph-based methods. To do this, one needs to reduce the information, lists of chemical reactions, available in databases. Even for the simplest type of graph representation, this reduction can be done in several ways. We investigate different simple network representations by testing how well they encode information about one biologically important network structure—network modularity (the propensity for edges to be clustered into dense groups that are sparsely connected between each other). To achieve this goal, we design a model of reaction systems where network modularity can be controlled and measure how well the reduction to simple graphs captures the modular structure of the model reaction system. We find that the network types that best capture the modular structure of the reaction system are substrate–product networks (where substrates are linked to products of a reaction) and substance networks (with edges between all substances participating in a reaction). Furthermore, we argue that the proposed model for reaction systems with tunable clustering is a general framework for studies of how reaction systems are affected by modularity. To this end, we investigate statistical properties of the model and find, among other things, that it recreates correlations between degree and mass of the molecules.

  • 12.
    Holme, Petter
    Dept. of Energy Science,Sungkyunkwan University,Suwon 440-746,Korea.
    Model versions and fast algorithms for network epidemiology2014In: Journal of Logistical Engineering University, ISSN 1672-7843, Vol. 30, no 3, p. 1-7Article in journal (Refereed)
    Abstract [en]

    Network epidemiology has become a core framework for investigating the role of human contact patterns in the spreading of infectious diseases. In network epidemiology, one represents the contact structure as a network of nodes (individuals) connected by links (sometimes as a temporal network where the links are not continuously active) and the disease as a compartmental model (where individuals are assigned states with respect to the disease and follow certain transition rules between the states). In this paper, we discuss fast algorithms for such simulations and also compare two commonly used versions,one where there is a constant recovery rate (the number of individuals that stop being infectious per time is proportional to the number of such people);the other where the duration of the disease is constant. The results show that, for most practical purposes, these versions are qualitatively the same.

  • 13.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, 440–746 Suwon, Republic of Korea; Department of Sociology, Stockholm University, 10961 Stockholm, Sweden.
    Shadows of the susceptible-infectious-susceptible immortality transition in small networks2015In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 92, no 1, article id 012804Article in journal (Refereed)
    Abstract [en]

    Much of the research on the behavior of the SIS model on networks has concerned the infinite size limit; in particular the phase transition between a state where outbreaks can reach a finite fraction of the population, and a state where only a finite number would be infected. For finite networks, there is also a dynamic transition—the immortality transition—when the per-contact transmission probability λ reaches 1. If λ < 1, the probability that an outbreak will survive by an observation time t tends to zero as t → ∞; if λ = 1, this probability is 1. We show that treating λ = 1 as a critical point predicts the λ dependence of the survival probability also for more moderate λ values. The exponent, however, depends on the underlying network. This fact could, by measuring how a vertex’s deletion changes the exponent, be used to evaluate the role of a vertex in the outbreak. Our work also confirms an extremely clear separation between the early die-off (from the outbreak failing to take hold in the population) and the later extinctions (corresponding to rare stochastic events of several consecutive transmission events failing to occur).

  • 14.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Computational Biology, Royal Institute of Technology, Stockholm.
    Signatures of currency vertices2009In: Journal of the Physical Society of Japan, ISSN 0031-9015, E-ISSN 1347-4073, Vol. 78, no 3, p. 034801-Article in journal (Refereed)
    Abstract [en]

    Many real-world networks have broad degree distributions. For some systems, this means that the functional significance of the vertices is also broadly distributed, in other cases the vertices are equally significant, but in different ways. One example of the latter case is metabolic networks, where the high-degree vertices — the currency metabolites — supply the molecular groups to the low-degree metabolites, and the latter are responsible for the higher-order biological function, of vital importance to the organism. In this paper, we propose a generalization of currency metabolites to currency vertices. We investigate the network structural characteristics of such systems, both in model networks and in some empirical systems. In addition to metabolic networks, we find that a network of music collaborations and a network of e-mail exchange could be described by a division of the vertices into currency vertices and others.

  • 15.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Sociology, Stockholm University, 10691 Stockholm, Sweden.
    Social, sexual and economic networks of prostitution2012In: Leonardo: Journal of the International Society for the Arts, Sciences and Technology, ISSN 0024-094X, E-ISSN 1530-9282, Vol. 45, no 1, p. 80-81Article in journal (Refereed)
    Abstract [en]

    This article discusses the networks of prostitution, in particular those that can be extracted from online data, and what they can teach us about prostitution itself, disease spreading, cultural differences and a broader spectrum of socio-economical phenomena.

  • 16.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Temporal networks2014In: Encyclopedia of Social Network Analysis and Mining / [ed] Reda Alhajj, Jon Rokne, Springer-Verlag New York, 2014, p. 2119-2128Chapter in book (Refereed)
  • 17.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Utbrotten av aktivitet och vårt förutsägbara beteende2012In: Framtider, ISSN 0281-0492, no 3, p. 24-26Article in journal (Other (popular science, discussion, etc.))
  • 18.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Ghoshal, Gourab
    University of Michigan.
    The Diplomat's Dilemma: Maximal Power for Minimal Effort in Social Networks2009In: Adaptive networks: theory, models and applications / [ed] Thilo Gross, Hiroki Sayama, Springer Berlin/Heidelberg, 2009, p. 269-288Chapter in book (Refereed)
    Abstract [en]

    Closeness is a global measure of centrality in networks, and a proxy for how influential actors are in social networks. In most network models, and many empirical networks, closeness is strongly correlated with degree. However, in social networks there is a cost of maintaining social ties. This leads to a situation (that can occur in the professional social networks of executives, lobbyists, diplomats and so on) where agents have the conflicting objectives of aiming for centrality while simultaneously keeping the degree low. We investigate this situation in an adaptive network-evolution model where agents optimize their positions in the network following individual strategies, and using only local information. The strategies are also optimized, based on the success of the agent and its neighbors. We measure and describe the time evolution of the network and the agents' strategies.

  • 19. Holme, Petter
    et al.
    Grönlund, Andreas
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Modelling the dynamics of youth subcultures2005In: JASSS: Journal of Artificial Societies and Social Simulation, ISSN 1460-7425, E-ISSN 1460-7425, Vol. 8, no 3Article in journal (Refereed)
    Abstract [en]

    What are the dynamics behind youth subcultures such as punk, hippie, or hip-hop cultures? How does the global dynamics of these subcultures relate to the individual's search for a personal identity? We propose a simple dynamical model to address these questions and find that only a few assumptions of the individual's behaviour are necessary to regenerate known features of youth culture.

  • 20.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Huss, Mikael
    Substance networks are optimal simple-graph representations of metabolism2010In: Chinese Science Bulletin, ISSN 1001-6538, E-ISSN 1861-9541, Vol. 55, no 27-28, p. 3161-3168Article in journal (Refereed)
    Abstract [en]

    One approach to study the system-wide organization of biochemistry is to use statistical graph theory. In such heavily simplified methods, which disregard most of the dynamic aspects of biochemistry, one is faced with fundamental questions. One such question is how the chemical reaction systems should be reduced to a graph retaining as much functional information as possible from the original reaction system. In these graph representations, should the edges go between substrates and products, or substrates and substrates, or both? Should vertices represent substances or reactions? Different representations encode different information about the reaction system and affect network measures in different ways. This paper investigates which representation reflects the functional organization of the metabolic system in the best way, according to the defined criteria. Four different graph representations of metabolism (three where the vertices are metabolites, one where the vertices are reactions) are evaluated using data from different organisms and databases. The graph representations are evaluated by comparing the overlap between clusters (network modules) and annotated functions, and also by comparing the set of identified currency metabolites with those that other authors have identified using qualitative biological arguments. It is found that a “substance network”, where all metabolites participating in a reaction are connected, is better than others, evaluated with respect to both the functional overlap between modules and functions and to the number and identity of the identified currency metabolites.

  • 21.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Huss, Mikael
    Science of Life Laboratory.
    Understanding and Exploiting Information Spreading and Integrating Technologies2011In: Journal of Computer Science and Technology, ISSN 1000-9000, E-ISSN 1860-4749, Vol. 26, no 5, p. 829-836Article in journal (Refereed)
    Abstract [en]

    Our daily life leaves an increasing amount of digital traces, footprints that are improving our lives. Data-mining tools, like recommender systems, convert these traces to information for aiding decisions in an ever-increasing number of areas in our lives. The feedback loop from what we do, to the information this produces, to decisions what to do next, will likely be an increasingly important factor in human behavior on all levels from individuals to societies. In this essay, we review some effects of this feedback and discuss how to understand and exploit them beyond mapping them on more well-understood phenomena. We take examples from models of spreading phenomena in social media to argue that analogies can be deceptive, instead we need to fresh approaches to the new types of data, something we exemplify with promising applications in medicine.

  • 22.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Huss, Mikael
    Jeong, Hawoong
    Subnetwork hierarchies of biochemical pathways2003In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 19, no 4, p. 532-538Article in journal (Refereed)
    Abstract [en]

    Motivation: The vastness and complexity of the biochemical networks that have been mapped out by modern genomics calls for decomposition into subnetworks. Such networks can have inherent non-local features that require the global structure to be taken into account in the decomposition procedure. Furthermore, basic questions such as to what extent the network (graph theoretically) can be said to be built by distinct subnetworks are little studied.

    Results: We present a method to decompose biochemical networks into subnetworks based on the global geometry of the network. This method enables us to analyze the full hierarchical organization of biochemical networks and is applied to 43 organisms from the WIT database. Two types of biochemical networks are considered: metabolic networks and whole-cellular networks (also including for example information processes). Conceptual and quantitative ways of describing the hierarchical ordering are discussed. The general picture of the metabolic networks arising from our study is that of a few core-clusters centred around the most highly connected substances enclosed by other substances in outer shells, and a few other well-defined subnetworks.

  • 23.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Huss, Mikael
    Science for Life Laboratory Stockholm.
    Lee, Sang Hoon
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Atmospheric Reaction Systems as Null-Models to Identify Structural Traces of Evolution in Metabolism2011In: PLoS ONE, ISSN 1932-6203, Vol. 6, no 5, p. e19759-Article in journal (Refereed)
    Abstract [en]

    The metabolism is the motor behind the biological complexity of an organism. One problem of characterizing its large-scale structure is that it is hard to know what to compare it to. All chemical reaction systems are shaped by the same physics that gives molecules their stability and affinity to react. These fundamental factors cannot be captured by standard null-models based on randomization. The unique property of organismal metabolism is that it is controlled, to some extent, by an enzymatic machinery that is subject to evolution. In this paper, we explore the possibility that reaction systems of planetary atmospheres can serve as a null-model against which we can define metabolic structure and trace the influence of evolution. We find that the two types of data can be distinguished by their respective degree distributions. This is especially clear when looking at the degree distribution of the reaction network (of reaction connected to each other if they involve the same molecular species). For the Earth's atmospheric network and the human metabolic network, we look into more detail for an underlying explanation of this deviation. However, we cannot pinpoint a single cause of the difference, rather there are several concurrent factors. By examining quantities relating to the modular-functional organization of the metabolism, we confirm that metabolic networks have a more complex modular organization than the atmospheric networks, but not much more. We interpret the more variegated modular arrangement of metabolism as a trace of evolved functionality. On the other hand, it is quite remarkable how similar the structures of these two types of networks are, which emphasizes that the constraints from the chemical properties of the molecules has a larger influence in shaping the reaction system than does natural selection.

  • 24.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Kim, Beom Jun
    Fodor, Viktoria
    Heterogeneous attachment strategies optimize the topology of dynamic wireless networks2010In: European Physical Journal B: Condensed Matter Physics, ISSN 1434-6028, E-ISSN 1434-6036, Vol. 73, no 4, p. 597-604Article in journal (Refereed)
    Abstract [en]

    In optimizing the topology of wireless networks built of a dynamic set of spatially embedded agents, there are many trade-offs to be dealt with. The network should preferably be as small (in the sense that the average, or maximal, pathlength is short) as possible, it should be robust to failures, not consume too much power, and so on. In this paper, we investigate simple models of how agents can choose their neighbors in such an environment. In our model of attachment, we can tune from one situation where agents prefer to attach to others in closest proximity, to a situation where agents attach to random others regardless of distance (which thus are, on average, further away than the connections to the spatial neighbors). We evaluate this scenario with several performance measures and find that the optimal topologies, for most of the quantities, is obtained for strategies resulting in a mix of most local and a few random connections.

  • 25.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Liljeros, Fredrik
    Birth and death of links control disease spreading in empirical contact networks2014In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 4, p. 4999-Article in journal (Refereed)
    Abstract [en]

    We investigate what structural aspects of a collection of twelve empirical temporal networks of human contacts are important to disease spreading. We scan the entire parameter spaces of the two canonical models of infectious disease epidemiology-the Susceptible-Infectious-Susceptible (SIS) and Susceptible-Infectious-Removed (SIR) models. The results from these simulations are compared to reference data where we eliminate structures in the interevent intervals, the time to the first contact in the data, or the time from the last contact to the end of the sampling. The picture we find is that the birth and death of links, and the total number of contacts over a link, are essential to predict outbreaks. On the other hand, the exact times of contacts between the beginning and end, or the interevent interval distribution, do not matter much. In other words, a simplified picture of these empirical data sets that suffices for epidemiological purposes is that links are born, is active with some intensity, and die.

  • 26.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Masuda, Naoki
    Umeå University, Faculty of Science and Technology, Department of Physics.
    The Basic Reproduction Number as a Predictor for Epidemic Outbreaks in Temporal Networks2015In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 10, no 3, article id e0120567Article in journal (Refereed)
    Abstract [en]

    The basic reproduction number R-0-the number of individuals directly infected by an infectious person in an otherwise susceptible population-is arguably the most widely used estimator of how severe an epidemic outbreak can be. This severity can be more directly measured as the fraction of people infected once the outbreak is over, Omega. In traditional mathematical epidemiology and common formulations of static network epidemiology, there is a deterministic relationship between R-0 and Omega. However, if one considers disease spreading on a temporal contact network-where one knows when contacts happen, not only between whom-then larger R-0 does not necessarily imply larger Omega. In this paper, we numerically investigate the relationship between R-0 and Omega for a set of empirical temporal networks of human contacts. Among 31 explanatory descriptors of temporal network structure, we identify those that make R-0 an imperfect predictor of Omega. We find that descriptors related to both temporal and topological aspects affect the relationship between R-0 and Omega, but in different ways.

  • 27.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Komplexa system och nätverksfysik: Om hur saker hänger ihop ger andra saker deras egenskaper2009In: Kosmos, Fysikersamfundet , 2009Chapter in book (Other (popular science, discussion, etc.))
    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.

  • 28.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Saramäki, Jari
    Aalto University.
    Temporal networks2012In: Physics reports, ISSN 0370-1573, E-ISSN 1873-6270, Vol. 519, no 3, p. 97-125Article, review/survey (Refereed)
    Abstract [en]

    A great variety of systems in nature, society and technology–from the web of sexual contacts to the Internet, from the nervous system to power grids–can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names—temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology—rather, we want to make papers readable across disciplines.

  • 29.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Saramäki, JariAalto University.
    Temporal Networks2013Collection (editor) (Refereed)
    Abstract [en]

    The concept of temporal networks is an extension of complex networks as a modeling framework to include information on when interactions between nodes happen. Many studies of the last decade examine how the static network structure affect dynamic systems on the network. In this traditional approach  the temporal aspects are pre-encoded in the dynamic system model. Temporal-network methods, on the other hand, lift the temporal information from the level of system dynamics to the mathematical representation of the contact network itself. This framework becomes particularly useful for cases where there is a lot of structure and heterogeneity both in the timings of interaction events and the network topology. The advantage compared to common static network approaches is the ability to design more accurate models in order to explain and predict large-scale dynamic phenomena (such as, e.g., epidemic outbreaks and other spreading phenomena). On the other hand, temporal network methods are mathematically and conceptually more challenging. This book is intended as a first introduction and state-of-the art overview of this rapidly emerging field.

  • 30.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Saramäki, Jari
    Aalto University.
    Temporal Networks as a Modeling Framework2013In: Temporal Networks / [ed] Petter Holme, Jari Saramäki, Berlin: Springer, 2013, p. 1-14Chapter in book (Refereed)
    Abstract [en]

    Life, at many levels, is about large connected systems. In the biological sense, life is a consequence of macromolecules building cells and carrying information. More mundanely, our everyday life happens amid a network of friends, acquaintances and colleagues. To understand life, at every level, we need to zoom out from macromolecules or friendships and look at their global organization from a distance. Here, zooming out means discarding less relevant information in a systematic way. One approach to this, successful the last decade, is network modeling. This means that one focusses on the units of the system, be it proteins or persons, and how they are connected, and nothing else. Of course, this is a very strong simplification. Often, one has more information about a system that would enrich rather than obscure the picture. One such additional type of information regards when the interactions happen between the units. The essence of temporal network modeling is to zoom out by excluding all information except which pairs of units are in contact and when these contacts happen.

  • 31.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, Suwon, Korea; Department of Sociology, Stockholm University.
    Takaguchi, Taro
    Time evolution of predictability of epidemics on networks2015In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 91, no 4, article id 042811Article in journal (Refereed)
    Abstract [en]

    Epidemic outbreaks of new pathogens, or known pathogens in new populations, cause a great deal of fear because they are hard to predict. For theoretical models of disease spreading, on the other hand, quantities characterizing the outbreak converge to deterministic functions of time. Our goal in this paper is to shed some light on this apparent discrepancy. We measure the diversity of (and, thus, the predictability of) outbreak sizes and extinction times as functions of time given different scenarios of the amount of information available. Under the assumption of perfect information-i.e., knowing the state of each individual with respect to the disease-the predictability decreases exponentially, or faster, with time. The decay is slowest for intermediate values of the per-contact transmission probability. With a weaker assumption on the information available, assuming that we know only the fraction of currently infectious, recovered, or susceptible individuals, the predictability also decreases exponentially most of the time. There are, however, some peculiar regions in this scenario where the predictability decreases. In other words, to predict its final size with a given accuracy, we would need increasingly more information about the outbreak.

  • 32.
    Holme, Petter
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Wu, Zhi-Xi
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Minnhagen, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Multiscaling in an YX model of networks2009In: 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. 80, p. 036120-Article in journal (Refereed)
    Abstract [en]

    Weinvestigate a Hamiltonian model of networks. The model is amirror formulation of the XY model (hence the name)—instead ofletting the XY spins vary, keeping the coupling topology static,we keep the spins conserved and sample different underlying networks.Our numerical simulations show complex scaling behaviors with various exponentsas the system grows and temperature approaches zero, but nofinite-temperature universal critical behavior. The ground-state and low-order excitations forsparse, finite graphs are a fragmented set of isolated networkclusters. Configurations of higher energy are typically more connected. Theconnected networks of lowest energy are stretched out giving thenetwork large average distances. For the finite sizes we investigate,there are three regions—a low-energy regime of fragmented networks, anintermediate regime of stretched-out networks, and a high-energy regime ofcompact, disordered topologies. Scaling up the system size, the bordersbetween these regimes approach zero temperature algebraically, but different network-structuralquantities approach their T=0 values with different exponents. We arguethis is a, perhaps rare, example of a statistical-physics modelwhere finite sizes show a more interesting behavior than thethermodynamic limit.

  • 33. Karimi, Fariba
    et al.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    A temporal network version of Watts’s cascade model2013In: Temporal Networks / [ed] Petter Holme, Jari Saramäki, Berlin: Springer, 2013, p. 315-330Chapter in book (Refereed)
    Abstract [en]

    Threshold models of cascades in the social and economical sciences explain the spread of opinion and innovation as an eect of social influence. In threshold cascade models, fads or innovation spread between agents as determined by their interactions to other agents and their personal threshold of resistance. Typically, these models do not account for structure in the timing of interaction between the units. In this work, we extend a model of social cascades by Duncan Watts to temporal interaction networks. In our model, we assume agents are influenced by their friends and acquaintances at certain time into the past. That is, the influence of the past ages and becomes unimportant. Thus, our modified cascade model has an eective time window of influence.We explore two types of thresholds—thresholds to fractions of the neighbors, or absolute numbers. We try our model on six empirical datasets and compare them with null models.

  • 34.
    Karimi, Fariba
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, Suwon 440-746, Korea and Department of Sociology, Stockholm University, 10691 Stockholm, Sweden.
    Threshold model of cascades in empirical temporal networks2013In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 392, no 16, p. 3476-3483Article in journal (Refereed)
    Abstract [en]

    Threshold models try to explain the consequences of social influence like the spread of fads and opinions. Along with models of epidemics, they constitute a major theoretical framework of social spreading processes. In threshold models on static networks, an individual changes her state if a certain fraction of her neighbors has done the same. When there are strong correlations in the temporal aspects of contact patterns, it is useful to represent the system as a temporal network. In such a system, not only contacts but also the time of the contacts are represented explicitly. In many cases, bursty temporal patterns slow down disease spreading. However, as we will see, this is not a universal truth for threshold models. In this work we propose an extension of Watts’s classic threshold model to temporal networks. We do this by assuming that an agent is influenced by contacts which lie a certain time into the past. I.e., the individuals are affected by contacts within a time window. In addition to thresholds in the fraction of contacts, we also investigate the number of contacts within the time window as a basis for influence. To elucidate the model’s behavior, we run the model on real and randomized empirical contact datasets.

  • 35.
    Karimi, Fariba
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Ramenzoni, Verónica
    Donders Institute for Brain, Cognition, and Behavior, Radboud University, The Netherlands.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Department of Energy Science, Sungkyunkwan University, Suwon 440-746, Republic of Korea and Department of Sociology, Stockholm University, 10691 Stockholm, Sweden.
    Structural differences between open and direct communication in an online community2014In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 414, no 15 November, p. 263-273Article in journal (Refereed)
    Abstract [en]

    Most research of online communication focuses on modes of communication that are either open (like forums, bulletin boards, Twitter, etc.) or direct (like e-mails). In this work, we study a dataset that has both types of communication channels. We relate our findings to theories of social organization and human dynamics. The data comprises 36,492 users of a movie discussion community. Our results show that there are differences in the way users communicate in the two channels that are reflected in the shape of degree- and interevent time distributions. The open communication that is designed to facilitate conversations with any member shows a broader degree distribution and more of the triangles in the network are primarily formed in this mode of communication. The direct channel is presumably preferred by closer communication and the response time in dialogs is shorter. On a more coarse-grained level, there are common patterns in the two networks. The differences and overlaps between communication networks, thus, provide a unique window into how social and structural aspects of communication establish and evolve.

  • 36.
    Lee, Eun
    et al.
    Sungkyunkwan University.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Network characteristics of individual pigments in cyanobacterial photosystem II core complexes2013In: Journal of the Korean Physical Society, ISSN 0374-4884, E-ISSN 1976-8524, Vol. 63, no 11, p. 2255-2261Article in journal (Refereed)
    Abstract [en]

    Part of the excitation energy transfer (EET) characteristics of the photosystem II (PSII) comes from the interconnection between pigments. To understand the correlation between the EET and the pigments’ interaction structure, we construct a network from the EET rates which are related to both the distance between the pigments (chlorophylls and pheophytins) and their spatial orientations. Especially, we investigate how well the PS II core complex’s EET functionality can be explained by using only the network topology in Thermosynechococcus vulcanus 1.9 °A. Starting from the Förster theory, we construct a network of EET pathways. For an analysis of the network structure, we calculate common network-structural measures like betweenness centrality, eigenvector centrality and weighted clustering. These measures can reflect the role of individual pigments in the EET network. In our work, we found that some well-known properties were reproduced by the network analysis of the simplified network, which means that the topology of the network encodes functionally relevant information. For example, from the network structural analysis, we can infer that most of the chlorophyll molecules (clorophylls) in the pigment-protein complex CP47 have heightened probability to transfer energy compared with other chlorophylls. We also see that the active branch chlorophylls in the reaction center are characterized by a high eigenvector centrality, a high betweenness centrality and a low weighted clustering coefficient. This is indicative of functionally important vertices.

  • 37.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Bernhardsson, Sebastian
    FOI, Swedish Defence Research Agency, Tumba SE-14725, Sweden.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Kim, Beom Jun
    Department of Physics, Sungkyunkwan University, Suwon 440-746, Korea.
    Minnhagen, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Neutral theory of chemical reaction networks2012In: New Journal of Physics, ISSN 1367-2630, E-ISSN 1367-2630, Vol. 14, p. 033032-Article in journal (Refereed)
    Abstract [en]

    To what extent do the characteristic features of a chemical reaction network reflect its purpose and function? In general, one argues that correlations between specific features and specific functions are key to understanding a complex structure. However, specific features may sometimes be neutral and uncorrelated with any system-specific purpose, function or causal chain. Such neutral features are caused by chance and randomness. Here we compare two classes of chemical networks: one that has been subjected to biological evolution (the chemical reaction network of metabolism in living cells) and one that has not (the atmospheric planetary chemical reaction networks). Their degree distributions are shown to share the very same neutral system-independent features. The shape of the broad distributions is to a large extent controlled by a single parameter, the network size. From this perspective, there is little difference between atmospheric and metabolic networks; they are just different sizes of the same random assembling network. In other words, the shape of the degree distribution is a neutral characteristic feature and has no functional or evolutionary implications in itself; it is not a matter of life and death.

  • 38.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    A greedy-navigator approach to navigable city plans2013In: The European Physical Journal Special Topics, ISSN 1951-6355, E-ISSN 1951-6401, Vol. 215, no 1, p. 135-144Article in journal (Refereed)
    Abstract [en]

    We use a set of four theoretical navigability indices for street maps to investigate the shape of the resulting street networks, if they are grown by optimizing these indices. The indices compare the performance of simulated navigators (having a partial information about the surroundings, like humans in many real situations) to the performance of optimally navigating individuals. We show that our simple greedy shortcut construction strategy generates the emerging structures that are different from real road network, but not inconceivable. The resulting city plans, for all navigation indices, share common qualitative properties such as the tendency for triangular blocks to appear, while the more quantitative features, such as degree distributions and clustering, are characteristically different depending on the type of metrics and routing strategies. We show that it is the type of metrics used which determines the overall shapes characterized by structural heterogeneity, but the routing schemes contribute to more subtle details of locality, which is more emphasized in case of unrestricted connections when the edge crossing is allowed.

  • 39.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Exploring maps with greedy navigators2012In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 108, no 12, p. 8701-Article in journal (Refereed)
    Abstract [en]

    During the last decade of network research focusing on structural and dynamical properties of networks, the role of network users has been more or less underestimated from the bird’s-eye view of global perspective. In this era of global positioning system equipped smartphones, however, a user’s ability to access local geometric information and find efficient pathways on networks plays a crucial role, rather than the globally optimal pathways. We present a simple greedy spatial navigation strategy as a probe to explore spatial networks. These greedy navigators use directional information in every move they take, without being trapped in a dead end based on their memory about previous routes. We suggest that the centralities measures have to be modified to incorporate the navigators’ behavior, and present the intriguing effect of navigators’ greediness where removing some edges may actually enhance the routing efficiency, which is reminiscent of Braess’s paradox. In addition, using samples of road structures in large cities around the world, it is shown that the navigability measure we define reflects unique structural properties, which are not easy to predict from other topological characteristics. In this respect, we believe that our routing scheme significantly moves the routing problem on networks one step closer to reality, incorporating the inevitable incompleteness of navigators’ information.

  • 40.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Geometric properties of graph layouts optimized for greedy navigation2012In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 86, no 6, p. 067103-Article in journal (Refereed)
    Abstract [en]

    The graph layouts used for complex network studies have been mainly developed to improve visualization. If we interpret the layouts in metric spaces such as Euclidean ones, however, the embedded spatial information can be a valuable cue for various purposes. In this work, we focus on encoding useful navigational information to geometric coordinates of vertices of spatial graphs, which is a reverse problem of harnessing geometric information for better navigation. In other words, the coordinates of the vertices are a map of the topology, not the other way around. We use a recently developed user-centric navigation protocol to explore spatial layouts of complex networks that are optimal for navigation. These layouts are generated with a simple simulated annealing optimization technique. We compare these layouts to others targeted at better visualization and discuss the spatial statistical properties of the optimized layouts for better navigability and its implication.

  • 41.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Pathlength scaling in graphs with incomplete navigational information2011In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371, E-ISSN 1873-2119, Vol. 390, no 21-22, p. 3996-4001Article in journal (Refereed)
    Abstract [en]

    The graph-navigability problem concerns how one can find as short paths as possible between a pair of vertices, given an incomplete picture of a graph. We study the navigability of graphs where the vertices are tagged by a number (between 1 and the total number of vertices) in a way to aid navigation. This information is too little to ensure errorfree navigation but enough, as we will show, for the agents to do significantly better than a random walk. In our setup, given a graph, we first assign information to the vertices that agents can utilize for their navigation. To evaluate the navigation, we calculate the average distance traveled over random pairs of source and target and different graph realizations. We show that this type of embedding can be made quite efficiently; the more information is embedded, the more efficient it gets. We also investigate the embedded navigational information in a standard graph layout algorithm and find that although this information does not make algorithms as efficient as the above-mentioned schemes, it is significantly helpful.

  • 42.
    Lee, Sang Hoon
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Lee, Sungmin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Son, Seung-Woo
    Department of Applied Physics, Hanyang University, Korea.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Phase-shift inversion in oscillator systems with periodically switching couplings2012In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 85, no 2, p. 027202-Article in journal (Refereed)
    Abstract [en]

    A system's response to external periodic changes can provide crucial information about its dynamical properties. We investigate the synchronization transition, an archetypical example of a dynamic phase transition, in the framework of such a temporal response. The Kuramoto model under periodically switching interactions has the same type of phase transition as the original mean-field model. Furthermore, we see that the signature of the synchronization transition appears in the relative delay of the order parameter with respect to the phase of oscillating interactions as well. Specifically, the phase shift becomes significantly larger as the system gets closer to the phase transition, so that the order parameter at the minimum interaction density can even be larger than that at the maximum interaction density, counterintuitively. We argue that this phase-shift inversion is caused by the diverging relaxation time, in a similar way to the resonance near the critical point in the kinetic Ising model. Our result, based on exhaustive simulations on globally coupled systems as well as scale-free networks, shows that an oscillator system's phase transition can be manifested in the temporal response to the topological dynamics of the underlying connection structure.

  • 43.
    Lee, Sungmin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Wu, Zhi-Xi
    Lanzhou Univ, Inst Computat Phys & Complex Systems.
    Cooperation, structure, and hierarchy in multiadaptive games2011In: Physical Review E. Statistical, Nonlinear, and Soft Matter Physics, ISSN 1539-3755, E-ISSN 1550-2376, Vol. 84, no 6, p. 061148-061158Article in journal (Refereed)
    Abstract [en]

    Game-theoretical models where the rules of the game and the interaction structure both coevolve with the game dynamics—multiadaptive games—capture very flexible situations where cooperation among selfish agents can emerge. In this work, we will discuss a multiadaptive model presented in a recent Letter [Phys. Rev. Lett. 106 028702 (2011)] as well as generalizations of it. The model captures a nonequilibrium situation where social unrest increases the incentive to cooperate and, simultaneously, agents are partly free to influence with whom they interact. First, we investigate the details of how feedback from the behavior of agents determines the emergence of cooperation and hierarchical contact structures. We also study the stability of the system to different types of noise, and find that different regions of parameter space show very different response. Some types of noise can destroy an all-cooperator (C) state. If, on the other hand, hubs are stable, then so is the all-C state. Finally, we investigate the dependence of the ratio between the time scales of strategy updates and the evolution of the interaction structure. We find that a comparatively fast strategy dynamics is a prerequisite for the emergence of cooperation.

  • 44.
    Lee, Sungmin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Wu, Zhi-Xi
    Lanzhou University.
    Emergent hierarchical structures in multiadaptive games2011In: Physical Review Letters, ISSN 0031-9007, E-ISSN 1079-7114, Vol. 106, no 2, p. 028702-4Article in journal (Refereed)
    Abstract [en]

    We investigate a game-theoretic model of a social system where both the rules of the game and the interaction structure are shaped by the behavior of the agents. We call this type of model, with several types of feedback couplings from the behavior of the agents to their environment, a multiadaptive game. Our model has a complex behavior with several regimes of different dynamic behavior accompanied by different network topological properties. Some of these regimes are characterized by heterogeneous, hierarchical interaction networks, where cooperation and network topology coemerge from the dynamics.

  • 45.
    Lee, Sungmin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Ramenzoni, Veronica
    University of Virginia.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Emergence of collective memories2010In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 5, no 9, p. e12522-Article in journal (Refereed)
    Abstract [en]

    Background We understand the dynamics of the world around us as by associating pairs of events, where one event has some influence on the other. These pairs of events can be aggregated into a web of memories representing our understanding of an episode of history. The events and the associations between them need not be directly experienced—they can also be acquired by communication. In this paper we take a network approach to study the dynamics of memories of history.

    Methodology/Principal Findings First we investigate the network structure of a data set consisting of reported events by several individuals and how associations connect them. We focus our measurement on degree distributions, degree correlations, cycles (which represent inconsistencies as they would break the time ordering) and community structure. We proceed to model effects of communication using an agent-based model. We investigate the conditions for the memory webs of different individuals to converge to collective memories, how groups where the individuals have similar memories (but different from other groups) can form.

    Conclusions/Significance Our work outlines how the cognitive representation of memories and social structure can co-evolve as a contagious process. We generate some testable hypotheses including that the number of groups is limited as a function of the total population size.

  • 46.
    Lee, Sungmin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Rocha, Luis E C
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Liljeros, Fredrik
    Stockholm University.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Exploiting Temporal Network Structures of Human Interaction to Effectively Immunize Populations2012In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 7, no 5, p. e36439-Article in journal (Refereed)
    Abstract [en]

    Decreasing the number of people who must be vaccinated to immunize a community against an infectious disease could both save resources and decrease outbreak sizes. A key to reaching such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely than average to become infected and to spread the disease further. Fortunately, the very behavior that makes these people important to vaccinate can help us to localize them. Earlier studies have shown that one can use previous contacts to find people that are central in static contact networks. However, real contact patterns are not static. In this paper, we investigate if there is additional information in the temporal contact structure for vaccination protocols to exploit. We answer this affirmative by proposing two immunization methods that exploit temporal correlations and showing that these methods outperform a benchmark static-network protocol in four empirical contact datasets under various epidemic scenarios. Both methods rely only on obtainable, local information, and can be implemented in practice. For the datasets directly related to contact patterns of potential disease spreading (of sexually-transmitted and nosocomial infections respectively), the most efficient protocol is to sample people at random and vaccinate their latest contacts. The network datasets are temporal, which enables us to make more realistic evaluations than earlier studies—we use only information about the past for the purpose of vaccination, and about the future to simulate disease outbreaks. Using analytically tractable models, we identify two temporal structures that explain how the protocols earn their efficiency in the empirical data. This paper is a first step towards real vaccination protocols that exploit temporal-network structure—future work is needed both to characterize the structure of real contact sequences and to devise immunization methods that exploit these.

  • 47.
    Lee, Sungmin
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Rocha, Luis E C
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Liljeros, Fredrik
    Department of Sociology, Stockholm University.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Exploiting temporal network structures of human interaction to effectively immunize populationsManuscript (preprint) (Other academic)
    Abstract [en]

    If we can lower the number of people needed to vaccinate for a community to be immune against contagious diseases, we can save resources and life. A key to reach such a lower threshold of immunization is to find and vaccinate people who, through their behavior, are more likely to become infected and effective to spread the disease than the average. Fortunately, the very behavior that makes these people important to vaccinate can help us finding them. People you have met recently are more likely to be socially active and thus central in the contact pattern, and important to vaccinate. We propose two immunization schemes exploiting temporal contact patterns. Both of these rely only on obtainable, local information and could implemented in practice. We show that these schemes outperform benchmark protocols in four real data sets under various epidemic scenarios. The data sets are dynamic, which enables us to make more realistic evaluations than other studies - we use information only about the past to perform the vaccination and the future to simulate disease outbreaks. We also use models to elucidate the mechanisms behind how the temporal structures make our immunization protocols efficient.

  • 48.
    Lu, Xin
    et al.
    Karolinska Institute.
    Bengtsson, Linus
    Karolinska Institute.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Predictability of population displacement after the 2010 Haiti earthquake2012In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 109, no 29, p. 11576-11581Article in journal (Refereed)
    Abstract [en]

    Most severe disasters cause large population movements. These movements make it difficult for relief organizations to efficiently reach people in need. Understanding and predicting the locations of affected people during disasters is key to effective humanitarian relief operations and to long-term societal reconstruction. We collaborated with the largest mobile phone operator in Haiti (Digicel) and analyzed the movements of 1.9 million mobile phone users during the period from 42 d before, to 341 d after the devastating Haiti earthquake of January 12, 2010. Nineteen days after the earthquake, population movements had caused the population of the capital Port-au-Prince to decrease by an estimated 23%. Both the travel distances and size of people’s movement trajectories grew after the earthquake. These findings, in combination with the disorder that was present after the disaster, suggest that people’s movements would have become less predictable. Instead, the predictability of people’s trajectories remained high and even increased slightly during the three-month period after the earthquake. Moreover, the destinations of people who left the capital during the first three weeks after the earthquake was highly correlated with their mobility patterns during normal times, and specifically with the locations in which people had significant social bonds. For the people who left Port-au-Prince, the duration of their stay outside the city, as well as the time for their return, all followed a skewed, fat-tailed distribution. The findings suggest that population movements during disasters may be significantly more predictable than previously thought.

  • 49.
    Masuda, Naoki
    et al.
    University of Tokyo.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Predicting and controlling infectious disease epidemics using temporal networks2013In: F1000 Prime Reports, ISSN 2051-7599, Vol. 5, p. 6-Article, review/survey (Refereed)
    Abstract [en]

    Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

  • 50.
    Mondani, Hernan
    et al.
    Department of Sociology, Stockholm University, Stockholm, Sweden.
    Holme, Petter
    Umeå University, Faculty of Science and Technology, Department of Physics. Institute for Futures Studies, Stockholm, Sweden and Department of Energy Science, Sungkyunkwan University, Suwon, Korea.
    Liljeros, Fredrik
    Institute for Futures Studies, Stockholm, Sweden and Department of Sociology, Stockholm University, Stockholm, Sweden.
    Fat-Tailed Fluctuations in the Size of Organizations: The Role of Social Influence2014In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 9, no 7, article id e100527Article in journal (Refereed)
    Abstract [en]

    Organizational growth processes have consistently been shown to exhibit a fatter-than-Gaussian growth-rate distribution in a variety of settings. Long periods of relatively small changes are interrupted by sudden changes in all size scales. This kind of extreme events can have important consequences for the development of biological and socio-economic systems. Existing models do not derive this aggregated pattern from agent actions at the micro level. We develop an agent-based simulation model on a social network. We take our departure in a model by a Schwarzkopf et al. on a scale-free network. We reproduce the fat-tailed pattern out of internal dynamics alone, and also find that it is robust with respect to network topology. Thus, the social network and the local interactions are a prerequisite for generating the pattern, but not the network topology itself. We further extend the model with a parameter  that weights the relative fraction of an individual's neighbours belonging to a given organization, representing a contextual aspect of social influence. In the lower limit of this parameter, the fraction is irrelevant and choice of organization is random. In the upper limit of the parameter, the largest fraction quickly dominates, leading to a winner-takes-all situation. We recover the real pattern as an intermediate case between these two extremes.

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