Abstract
Since community structure is an important feature of complex network, community detection has attracted more and more attention in recent years. Most early researchers focus on identifying disjoint communities, whereas communities in many real networks are overlapped. In this paper, we propose a novel algorithm MCLC with random walks on line graph and attraction intensity to discover overlapping communities. MCLC algorithm first generates a weighted line graph from a undirected network, then divides links into “link communities” through random walks on the line graph. Finally, it transforms the “link communities” to “node communities” using the function of attraction intensity. The “node communities” are permitted overlapped, and the overlapping size is controlled by the threshold of attraction intensity. Experiments on some real world networks validate the effectiveness and efficiency of the proposed algorithm. Comparing overlapping modularity \(Q_{ov} \) with other related algorithms, the results of this algorithm is satisfactory.
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Acknowledgements
The author gratefully acknowledges support from National Natural Science Foundation of China projects of grant No. 61272149, 61379058, 61379057, 61350011 and JSPS A3 Foresight Program of JSPS KAKENHI Grant Number 26730056, 15K15976.
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Deng, X., Li, G., Dong, M. (2015). Finding Overlapping Communities with Random Walks on Line Graph and Attraction Intensity. In: Xu, K., Zhu, H. (eds) Wireless Algorithms, Systems, and Applications. WASA 2015. Lecture Notes in Computer Science(), vol 9204. Springer, Cham. https://doi.org/10.1007/978-3-319-21837-3_10
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DOI: https://doi.org/10.1007/978-3-319-21837-3_10
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