Overlapping Community Detection Based on Random Walk and Seeds Extension

As an important research direction in the complex social network, the difficulty of community detection lies in the search and discovery of social structures efficiently and accurately. In this study, an algorithm named SEOCD (Seeds Extension Overlapping Community Detection) for overlapping community detection based on random walk and seeds extension is proposed in order to solve the problem of seeds selection and expansion in many seed-based algorithms. First, SEOCD uses the random walk strategy to find the seed communities with tight structures. Second, from the seed communities, the similarity between each pair of node and community is calculated. The nodes whose similarity greater than a predefined threshold are selected. Third, The strategy of optimizing a self-adaptive function is used to expand the communities. Finally, The free nodes in the network are assigned to their corresponding communities, which finds out all the overlapping community structures. Experiments on real and artificial networks show that SEOCD is capable of discovering overlapping communities in complex social networks efficiently.

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