@inproceedings{e1cde3c80d9b40a5b783345c0295ccaf,
title = "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 dissimilarity 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 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.",
keywords = "Community detection, Density peaks, Evidential clustering, Theory of belief functions",
author = "Kuang Zhou and Quan Pan and Arnaud Martin",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 5th International Conference on Belief Functions: Theory and Applications, BELIEF 2018 ; Conference date: 17-09-2018 Through 21-09-2018",
year = "2018",
doi = "10.1007/978-3-319-99383-6_33",
language = "英语",
isbn = "9783319993829",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "269--277",
editor = "Sebastien Destercke and Fabio Cuzzolin and Arnaud Martin and Thierry Denoeux",
booktitle = "Belief Functions",
}