The many facets of community detection in complex networks.
Michael T. Schaub,Michael T. Schaub,Michael T. Schaub,Jean-Charles Delvenne,Martin Rosvall,Renaud Lambiotte +5 more
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In this paper, the authors provide a focused review of the different motivations that underpin community detection, highlighting the different facets of community detection and highlighting the many lines of research and points out open directions and avenues for future research.Abstract:
Community detection, the decomposition of a graph into essential building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of assortative community structure and classified based on the mathematical techniques they employ. However, this comparison can be misleading because apparent similarities in their mathematical machinery can disguise different goals and reasons for why we want to employ community detection in the first place. Here we provide a focused review of these different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research and points out open directions and avenues for future research.read more
Citations
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Dissertation
Détection de communautés orientée sommet pour des réseaux mobiles opportunistes sociaux
TL;DR: Dans ce contexte, nous proposons d'une part un principe global de fonctionnement original que nous mettons en oeuvre et declinons dans trois algorithmes dedies a trois configurations differentes of the tâche de detection de communautes : l'algorithme VOLCAN considere le cas of reference des communaute disjointes dans un graphe statique.
Journal ArticleDOI
Systemic centrality and systemic communities in financial networks
TL;DR: The analysis suggests that centrality measures and community identification methods complement eachother for assessing systemic risk in financial networks.
Journal ArticleDOI
From Free Text to Clusters of Content in Health Records: An Unsupervised Graph Partitioning Approach
TL;DR: Network-theoretical tools are applied to the analysis of free text in Hospital Patient Incident reports in the English National Health Service, to find clusters of reports in an unsupervised manner and at different levels of resolution based directly on the free text descriptions contained within them.
Proceedings ArticleDOI
Spectral Partitioning of Time-varying Networks with Unobserved Edges
TL;DR: A variant of ‘blind’ community detection, in which a network is partitioned from the observation of a (dynamical) graph signal defined on the network, is discussed, and a simple spectral algorithm is proposed for inferring the partition of the latent SBM.
Book ChapterDOI
Temporal Communication Motifs in Mobile Cohesive Groups
TL;DR: Through a methodology which identifies cohesive groups and extracts their temporal motifs, it is shown how the members of social groups interact by means of calls and text messages, where communication patterns between pairs of group members are predominant.
References
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Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
Journal ArticleDOI
Finding and evaluating community structure in networks.
TL;DR: It is demonstrated that the algorithms proposed are highly effective at discovering community structure in both computer-generated and real-world network data, and can be used to shed light on the sometimes dauntingly complex structure of networked systems.
Journal ArticleDOI
Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: In this paper, the authors proposed a simple method to extract the community structure of large networks based on modularity optimization, which is shown to outperform all other known community detection methods in terms of computation time.