Evidential community detection based on density peaks

Abstract : Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set. In order to find credal community structure in graph data sets, in this paper, we propose a novel evidential community detection algorithm based on density peaks (EDPC). Two new metrics, the local density ρ and the minimum dissimi-larity δ, are first defined for each node in the graph. Then the nodes with both higher ρ and δ values are identified as community centers. Finally, the remaing nodes are assigned with corresponding community labels through a simple two-step evidential label propagation strategy. The membership of each node is described in the form of basic belief assignments , which can well express the uncertainty included in the community structure of the graph. The experiments demonstrate the effectiveness of the proposed method on real-world networks.
Type de document :
Communication dans un congrès
BELIEF 2018 - The 5th International Conference on Belief Functions, Sep 2018, Compiègne, France
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https://hal.archives-ouvertes.fr/hal-01882803
Contributeur : Kuang Zhou <>
Soumis le : jeudi 27 septembre 2018 - 14:15:53
Dernière modification le : dimanche 30 septembre 2018 - 01:14:06

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EDPC_belief2018.pdf
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  • HAL Id : hal-01882803, version 1
  • ARXIV : 1809.10903

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Kuang Zhou, Quan Pan, Arnaud Martin. Evidential community detection based on density peaks. BELIEF 2018 - The 5th International Conference on Belief Functions, Sep 2018, Compiègne, France. 〈hal-01882803〉

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