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A Density-Based Seed-Centric Community Detection Algorithm

Although many different community detection algorithms have been proposed to detect community structures in complex networks, how to effectively detect community structures is still a great challenge. Seed-centric methods is one of the most effective solutions for community detection. To more, in this paper, we propose a novel density-based seed expansion algorithm, namely, DenSeC, which can easily find cores of communities. By taking nodes with higher density locally as cores of communities, DenSeC first detects cores by searching from sparsely connected area to densely connected area. Then, starting from cores, along the path from densely connected area to sparsely connected area, it constructs a community by iteratively adding nodes which are similar to the nodes in the community. Numerical experiments have verified the effectiveness of DenSeC.