Multiresolution Community Detection Based on Fuzzy Clustering

WANG Xiaofeng; LIU Gongshen; LI Jianhua

doi:10.11999/JEIT161116

Volume 39 Issue 9

Sep. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(9): 2033-2039

WANG Xiaofeng, LIU Gongshen, LI Jianhua. Multiresolution Community Detection Based on Fuzzy Clustering[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2033-2039. doi: 10.11999/JEIT161116

Citation:

WANG Xiaofeng, LIU Gongshen, LI Jianhua. Multiresolution Community Detection Based on Fuzzy Clustering[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2033-2039. doi: 10.11999/JEIT161116

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Multiresolution Community Detection Based on Fuzzy Clustering

doi: 10.11999/JEIT161116 cstr: 32379.14.JEIT161116

Funds:

The National 973 Key Basic Research Program of China (2013CB329603), The National Natural Science Foundation of China (61472248, 61431008)

Received Date: 2016-10-20
Rev Recd Date: 2017-05-10
Publish Date: 2017-09-19

Abstract

Abstract

Focusing on the complexity of network structure and the indeterminacy of community partition, this paper puts forward a novel fuzzy clustering method for uncovering community structures. In contrast to previous studies, the proposed method disposes the similarity of connecting vertices with fuzzy relation. Based on local interactive information, it considers the fuzzy relation between vertices and the transitive similarity in network topology to divide vertices into communities. In addition, multiresolution communities can be detected by adjusting fuzzy parameter. In order to avoid subjectivity in the selection of cluster number, a new modularity is introduced to evaluate the effectiveness of the clustering analysis. It is proved by experiments that the method is ef?cient and stable to detect underlying communities.
- Social network,
- Community structure,
- Fuzzy clustering,
- Structural similarity

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References(36)

References

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