Overlapping-communities Recognition Algorithm Based on Contribution Function

LIU Gongshen; MENG Kui; GUO Hongyi; SU Bo; LI Jianhua

doi:10.11999/JEIT161109

Volume 39 Issue 8

Aug. 2017

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LIU Gongshen, MENG Kui, GUO Hongyi, SU Bo, LI Jianhua . Overlapping-communities Recognition Algorithm Based on Contribution Function[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1964-1971. doi: 10.11999/JEIT161109

Citation:

LIU Gongshen, MENG Kui, GUO Hongyi, SU Bo, LI Jianhua . Overlapping-communities Recognition Algorithm Based on Contribution Function[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1964-1971. doi: 10.11999/JEIT161109

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Overlapping-communities Recognition Algorithm Based on Contribution Function

doi: 10.11999/JEIT161109

Funds:

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

Received Date: 2016-10-18
Rev Recd Date: 2017-04-24
Publish Date: 2017-08-19

Abstract

Abstract

Overlapping is one of the most important characteristics of real-world networks. Based on the classic labeling algorithm, the overlapping-community orientated label propagation algorithm based on contribution function is proposed. In this algorithm, each node is indicated by a set of triples (threshold, label, and coefficient). The threshold value of every node is used as a metric for labels decision, which is calculated automatically by multiple linear regression equation. The dependent coefficient is used to measure the relevance of the current node with the correspondent community which is marked by the label. A greater value of dependent coefficient means a stronger association between the node and the community. During each iteration process, the dependent coefficients are calculated through Contribution Function (CF) of each node, and new triples are produced. Then the labels in terms of decision rules are selected, and the dependent coefficients of the node are normalized. According to the tests with real-world networks and automatic generation of LFR (Lancichinetti Fortunato Radicchi) test network, the algorithm can divide communication with high accuracy and robust result.
- Complex networks,
- Communities detecting,
- Overlapping communities

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References(15)

References

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