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| Overlapping-communities Recognition Algorithm Based on Contribution Function |
| LIU Gongshen MENG Kui GUO Hongyi SU Bo LI Jianhua |
| (School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China) |
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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.
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Received: 18 October 2016
Published: 26 May 2017
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| Fund: The National 973 Key Basic Research Program of China (2013CB329603), The National Natural Science Foundation of China (61472248) |
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Corresponding Authors:
MENG Kui
E-mail: mengkui@sjtu.edu.cn
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