LGIEM: Global and local node influence based community detection

Abstract Community detection is one of the hot topics in the complex networks. It aims to find subgraphs that are internally dense but externally sparsely connected. In this paper, a new method is proposed to identify the most influential nodes which are considered as cores of communities and achieve the initial communities. Then, by an expansion strategy, unassigned nodes are added to initial communities to expand communities. Finally, merging overlapping communities to get the final community structure. To evaluate the performance of the proposed node influence method (LGI), the susceptible–infected–removed (SIR) diffusion model are used. Testing with the synthetic networks and real-world networks, LGI can identify best nodes with high influence and is better than other centrality methods. Finally, experiments show that our proposed community detection algorithm based on influential nodes (LGIEM) is able to detect communities efficiently, and achieves better performance compared to other recent methods.

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