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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.
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Contributor : Kuang Zhou <>
Submitted on : Thursday, September 27, 2018 - 2:15:53 PM
Last modification on : Friday, March 6, 2020 - 4:10:03 PM
Document(s) archivé(s) le : Friday, December 28, 2018 - 5:20:23 PM


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


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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