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  • 1.
    Aksnes, Dag W.
    et al.
    Nordic Institute for Studies in Innovation, Research & Education (NIFU), Norway.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Colliander, Cristian
    Umeå University, Faculty of Social Sciences, Department of Sociology. Umeå University, Umeå University Library.
    Nilsson, Lena Maria
    Umeå University, Arctic Research Centre at Umeå University. Umeå University, Faculty of Medicine, Department of Epidemiology and Global Health.
    Kullerud, Lars (Contributor)
    UArctic.
    Larson, Keith (Contributor)
    Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
    SCITE team, (Contributor)
    Arctic Research Trends: Bibliometrics 2016-20222023Report (Other academic)
    Abstract [en]

    This work was conducted by the UArctic Thematic Network on Research Analytics and Bibliometrics. It was supported by Global Affairs Canada through the Global Arctic Leadership Initiative.

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  • 2.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Through the coding-lens: community detection and beyond2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    We live in a highly-connected world and find networks wherever we look: social networks, public transport networks, telecommunication networks, financial networks, and more. These networks can be immensely complex, comprising potentially millions or even billions of inter-connected objects. Answering questions such as how to control disease spreading in contact networks, how to optimise public transport networks, or how to diversify investment portfolios requires understanding each network's function and working principles.

    Network scientists analyse the structure of networks in search of communities: groups of objects that form clusters and are more connected to each other than the rest. Communities form the building blocks of networks, corresponding to their sub-systems, and allow us to represent networks with coarse-grained models. Analysing communities and their interactions helps us unravel how networks function.

    In this thesis, we use the so-called map equation framework, an information-theoretic community-detection approach. The map equation follows the minimum description length principle and assumes complete data in networks with one node type. We challenge these assumptions and adapt the map equation for community detection in networks with two node types and incomplete networks where some data is missing. We move beyond detecting communities and derive approaches for how, based on communities, we can identify influential objects in networks, and predict links that do not (yet) exist.

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  • 3.
    Blöcker, Christopher
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Mejtoft, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Norgren, Nina
    Umeå University, Faculty of Medicine, Department of Molecular Biology (Faculty of Medicine). National Bioinformatics Infrastructure Sweden, Science for Life Laboratory, Umeå University, Umeå, Sweden.
    Python in a week: Conceptual tests for learning and course development2023In: Proceedings of the International CDIO Conference / [ed] Reidar Lyng; Jens Bennedsen; Lamjed Bettaieb; Nils Rune Bodsberg; Kristina Edström; María Sigríður Guðjónsdóttir; Janne Roslöf; Ole K. Solbjørg; Geir Øien, NTNU SEED , 2023, p. 470-480Conference paper (Refereed)
    Abstract [en]

    Programming has gradually become an essential skill for engineers and scientists across disciplines and is an important part of the CDIO Syllabus covering fundamental knowledge and reasoning. Recently, there has been a shift away from introductory programming languages like C and Java towards Python, especially in programs where the focus lies on handling and analysing large quantities of data, such as energy technology, biotechnology, and bioinformatics. This paper illustrates the successful setup of a one-week-long introductory Python programming course with a hands-on approach. Given the limited time, a challenge is how to effectively teach students a meaningful set of skills that enables them to self-guide their future learning. Moreover, since the course does not include any summative assessment, we need other means of measuring students’ learning and guiding course development. We address these challenges by coupling short lectures with short quizzes for formative assessment, adding another learning activity to the course. We find that, in the absence of summative assessment, short, frequent quizzes with immediate feedback are an excellent tool to track the learning of a class as a whole. Students report that the quizzes, albeit challenging, improved their understanding of programming concepts, made them aware of potential mistakes, and were a fun learning experience. Furthermore, the results from this paper illustrate how a new programming language can be taught to students without prior programming skills in a short period of time. We summarise our lessons learnt for designing and integrating quizzes in short-format programming courses.

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  • 4.
    Blöcker, Christopher
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Nieves, Juan Carlos
    Umeå University, Faculty of Science and Technology, Department of Computing Science.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Map equation centrality: community-aware centrality based on the map equation2022In: Applied Network Science, E-ISSN 2364-8228, Vol. 7, no 1, article id 56Article in journal (Refereed)
    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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  • 5.
    Blöcker, Christopher
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Mapping flows on bipartite networks2020In: Physical review. E, ISSN 2470-0045, E-ISSN 2470-0053, Vol. 102, no 5, article id 052305Article in journal (Refereed)
    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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  • 6.
    Blöcker, Christopher
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Smiljanic, Jelena
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Scholtes, Ingo
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Similarity-based Link Prediction from Modular Compression of Network FlowsManuscript (preprint) (Other academic)
    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.

  • 7.
    Blöcker, Christopher
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Smiljanic, Jelena
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Scholtes, Ingo
    Center for Artificial Intelligence and Data Science, University of Würzburg, Germany.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Similarity-based link prediction from modular compression of network flows2022In: Proceedings of the First Learning on Graphs Conference, ML Research Press , 2022, p. 52:1-52:18Conference paper (Refereed)
    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.

  • 8.
    Holmgren, Anton
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Mapping biased higher-order walks reveals overlapping communitiesManuscript (preprint) (Other academic)
    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.

  • 9.
    Mejtoft, Thomas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Cripps, Helen
    Edith Cowan University.
    Berglund, Stefan
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Sustainable international experience: A collaborative teaching project2020In: The 16th International CDIO Conference: Proceedings – Full Papers. Volume 2(2) / [ed] J. Malmqvist, J. Bennedsen, K. Edström, N. Kuptasthien, A. Sripakagorn, J. Roslöf, I. Saemundsdottir & M. Siiskonen, Gothenburg: Chalmers University of Technology/CDIO Initiative , 2020, Vol. 2, p. 196-205Conference paper (Refereed)
    Abstract [en]

    Within engineering education, there is an increasing need for providing our students withinternational experiences. This is most often done by exchange studies abroad. However, amajority of the students on engineering programs do not engage in any international exchange.This paper presents insights from a collaborative cross-disciplinary international project to givestudents international experience without having to travel. From both a sustainabilityperspective and a situation where e.g. a global virus outbreak stop students from travelling,solutions that give engineering students experience of working in an international setting arebecoming increasingly important. Initial challenges, for the teachers involved in the project,that were addressed before the project started, included the assessment of students, the useof online collaborative tools, assessment of students and the dependence between the twocourses. The learnings from the first and second iteration of the collaborative project weremainly focused around transparency, introduction of students to each other, communication,real-time issues and deadlines. By gradually remove these peripheral challenges for thestudents, resulting in making the students focus on the actual challenges surrounding theactual collaborative project. Even though this project is ongoing, the initial results clearly showthat by integrating courses between different countries and disciplines, it is possible to createan environment that strengthens the students’ ability in teamwork, communication andaddresses the cultural and professional aspects of working as an engineer in an internationalcontext. 

  • 10.
    Mejtoft, Thomas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Cripps, Helen
    Edith Cowan University, Perth, Australia.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    International Professional Skills: Interdisciplinary Project Work2022In: The 18th International CDIO Conference: Proceedings – Full Papers / [ed] Maria Sigridur Gudjonsdottir; Haraldur Audunsson; Arkaitz Manterola Donoso; Gudmundur Kristjansson; Ingunn Saemundsdóttir; Joseph Timothy Foley; Marcel Kyas; Angkee Sripakagorn; Janne Roslöf; Jens Bennedsen; Kristina Edström; Natha Kuptasthien; Reidar Lyng, Reykjavik: Reykjavik University , 2022, p. 453-464Conference paper (Refereed)
    Abstract [en]

    Higher education should provide learning situations that prepare students for a future profession and make them world-ready. This paper reports insights from an international interdisciplinary collaborative project aiming to create learning experiences that are close to a professional situation. The collaboration setup simulates a setting of a digital agency with a development team in Sweden and a marketing team in Australia working together to solve a task. The collaborative project has been active since 2017, completing its fifth iteration in 2021. Postcourse survey results show that the students felt that a real situation was created with a high level of collaboration and commitment, internationalization, well selected digital collaborative tools, and that an interdisciplinary community of practice was created among the students.

  • 11.
    Mejtoft, Thomas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Cripps, Helen
    Edith Cowan University.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Internationalization at home: An international interdisciplinary experience2021In: 8:e Utvecklingskonferensen för Sveriges ingenjörsutbildningar: Detaljerat program, 2021Conference paper (Refereed)
    Abstract [en]

    In today’s global society, international experience isimportant for students studying all subjects. This paper providesinsights and learnings from a long-term project with the purposeto provide international interdisciplinary experience forengineering students in Sweden as well as for marketing studentsin Australia. The paper discusses the design of the latest iterationof a long-term collaborative project that enables students who donot have the opportunity to engage in exchange studies in aprofessional international setting. The main objective of this paperis to give inspiration and a starting point to the implementation ofinternational learning experiences as an integrated part ofstudents’ education. 

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  • 12.
    Mejtoft, Thomas
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Cripps, Helen
    School of Business and Law, Edith Cowan University, Perth, Australia.
    Fong-Emmerson, Melissa
    School of Business and Law, Edith Cowan University, Perth, Australia.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Enhancing professional skills among engineering students by interdisciplinary international collaboration: Engineering Education for Sustainability2023In: Book of Proceedings for the 51st Annual Conference of the European Society for Engineering Education: Engineering Education for Sustainability, Proceedings / [ed] Ger Reilly; Mike Murphy, Balázs Vince Nagy; Hannu-Matti Järvinen, SEFI , 2023, p. 2486-2495Conference paper (Refereed)
    Abstract [en]

    Providing necessary knowledge and skills for engineering students to become successful professionals is a tricky task. Besides disciplinary knowledge, e.g., communication skills, ability to work in teams, and international experience are often mentioned as important. Regarding internationalization, most engineering programs in Sweden rely on either student exchange or low-level internationalization-at-home, such as international literature and lecturers. This paper explores sustainable international experiences for students on their home turf provided through an international interdisciplinary collaboration where engineering students in Sweden and marketing students in Australia work together on a project. The setup simulates a consultancy firm with development and marketing offices in different countries that cooperate to launch an application for the Australian market. The paper is based on interviews and surveys with students and teachers participating in this, since 2017, ongoing project.

    Findings reveal that students encountered several challenges that are hard to simulate in an ordinary university setting, e.g., language barriers, cultural differences, time differences, differences between disciplines, and varying work habits and values. The results also highlight opportunities such as learning from each other's perspectives and expertise, developing a more professional approach, presenting to people from other industry backgrounds, and gaining a better understanding of different cultures. The results show that the students gain professional experience that is of great value for their future profession. From a teacher's perspective, the paper discusses important issues when setting up an international inter-disciplinary collaboration, e.g., alignment of exercises, building a common ground, and the need for flexibility.

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  • 13.
    Smiljanic, Jelena
    et al.
    Umeå University, Faculty of Science and Technology, Department of Physics. Scientific Computing Laboratory, Center for the Study of Complex Systems, Institute of Physics Belgrade, University of Belgrade, Pregrevica 118, Belgrade, Serbia.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Edler, Daniel
    Umeå University, Faculty of Science and Technology, Department of Physics. Gothenburg Global Biodiversity Centre, Box 461, Gothenburg, Sweden; Department of Biological and Environmental Sciences, University of Gothenburg, Carl Skottsbergs Gata 22B, Gothenburg, Sweden.
    Rosvall, Martin
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Mapping flows on weighted and directed networks with incomplete observations2021In: Journal of Complex Networks, ISSN 2051-1310, E-ISSN 2051-1329, Vol. 9, no 6, article id cnab044Article in journal (Refereed)
    Abstract [en]

    Detecting significant community structure in networks with incomplete observations is challenging because the evidence for specific solutions fades away with missing data. For example, recent research shows that flow-based community detection methods can highlight spurious communities in sparse undirected and unweighted networks with missing links. Current Bayesian approaches developed to overcome this problem do not work for incomplete observations in weighted and directed networks that describe network flows. To overcome this gap, we extend the idea behind the Bayesian estimate of the map equation for unweighted and undirected networks to enable more robust community detection in weighted and directed networks. We derive an empirical Bayes estimate of the transitions rates that can incorporate metadata information and show how an efficient implementation in the community-detection method Infomap provides more reliable communities even with a significant fraction of data missing.

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  • 14.
    Thean, Lai Fun
    et al.
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore.
    Blöcker, Christopher
    Umeå University, Faculty of Science and Technology, Department of Physics.
    Li, Hui Hua
    Health Service Research Unit, Singapore General Hospital, Singapore, Singapore.
    Lo, Michelle
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore.
    Wong, Michelle
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore.
    Tang, Choong Leong
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore.
    Tan, Emile K. W.
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore.
    Rozen, Steven G.
    Duke-NUS Center for Computational Biology, Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
    Cheah, Peh Yean
    Department of Colorectal Surgery, Singapore General Hospital, Academia, Level 9, Discovery Tower, 20 College Road, Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore; Duke-NUS Medical School, National University of Singapore, Singapore, Singapore.
    Enhancer-derived long non-coding RNAs CCAT1 and CCAT2 at rs6983267 has limited predictability for early stage colorectal carcinoma metastasis2021In: Scientific Reports, E-ISSN 2045-2322, Vol. 11, no 1, article id 404Article in journal (Refereed)
    Abstract [en]

    Up-regulation of long non-coding RNAs (lncRNAs), colon-cancer associated transcript (CCAT) 1 and 2, was associated with worse prognosis in colorectal cancer (CRC). Nevertheless, their role in predicting metastasis in early-stage CRC is unclear. We measured the expression of CCAT1, CCAT2 and their oncotarget, c-Myc, in 150 matched mucosa-tumour samples of early-stage microsatellite-stable Chinese CRC patients with definitive metastasis status by multiplex real-time RT-PCR assay. Expression of CCAT1, CCAT2 and c-Myc were significantly up-regulated in the tumours compared to matched mucosa (p < 0.0001). The expression of c-Myc in the tumours was significantly correlated to time to metastasis [hazard ratio = 1.47 (1.10–1.97)] and the risk genotype (GG) of rs6983267, located within CCAT2. Expression of c-Myc and CCAT2 in the tumour were also significantly up-regulated in metastasis-positive compared to metastasis-negative patients (p = 0.009 and p = 0.04 respectively). Nevertheless, integrating the expression of CCAT1 and CCAT2 by the Random Forest classifier did not improve the predictive values of ColoMet19, the mRNA-based predictor for metastasis previously developed on the same series of tumours. The role of these two lncRNAs is probably mitigated via their oncotarget, c-Myc, which was not ranked high enough previously to be included in ColoMet19.

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