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Erschienen in:
2018 | OriginalPaper | Buchkapitel
Evidential Community Detection Based on Density Peaks
verfasst von : Kuang Zhou, Quan Pan, Arnaud Martin
Erschienen in: Belief Functions: Theory and Applications
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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 \(\rho \) and the minimum dissimilarity \(\delta \), are first defined for each node in the graph. Then the nodes with both higher \(\rho \) and \(\delta \) values are identified as community centers. Finally, the remaining 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.