Finding compact communities in large graphs

  title={Finding compact communities in large graphs},
  author={Jean Creusefond and Thomas Largillier and Sylvain Peyronnet},
  journal={2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
This article presents an efficient hierarchical clustering algorithm that solves the problem of core community detection. It is a variant of the standard community detection problem in which we are particularly interested in the connected core of communities. To provide a solution to this problem, we question standard definitions on communities and provide alternatives. We propose a function called compactness, designed to assess the quality of a solution to this problem. Our algorithm is based… Expand
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Metrics for Community Analysis
A survey of the start-of-the-art metrics used for the detection and the evaluation of community structure and a comparative analysis of these metrics in measuring the goodness of the underlying community structure is presented. Expand
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Mets a disposition un logiciel nomme CoDACom (Community Detection Algorithm Comparator, permettant d'appliquer cette methodologie ainsi that d'utiliser un grand nombre d'outils de detection de communautes. Expand


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Finding and evaluating community structure in networks.
  • M. Newman, M. Girvan
  • Computer Science, Physics
  • Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
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. Expand
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A hierarchical agglomeration algorithm for detecting community structure which is faster than many competing algorithms: its running time on a network with n vertices and m edges is O (md log n) where d is the depth of the dendrogram describing the community structure. Expand
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