A Local-Neighborhood Information Based Overlapping Community Detection Algorithm for Large-Scale Complex Networks

As the size of available networks is continuously increasing (even with millions of nodes), large-scale complex networks are receiving significant attention. While existing overlapping-community detection algorithms are quite effective in analyzing complex networks, most of these algorithms suffer from scalability issues when applied to large-scale complex networks, which can have more than 1,000,000 nodes. To address this problem, we propose an efficient local-expansion-based overlapping-community detection algorithm using local-neighborhood information (OCLN). During the iterative expansion process, only neighbors of nodes added in the last iteration (rather than all neighbors) are considered to determine whether they can join the community. This significantly reduces the computational cost and enhances the scalability for community detection in large-scale networks. A belonging coefficient is also proposed in OCLN to filter out incorrectly identified nodes. Theoretical analysis demonstrates that the computational complexity of the proposed OCLN is linear with respect to the size of the network to be detected. Experiments on large-scale LFR benchmark and real-world networks indicate the effectiveness of OCLN for overlapping-community detection in large-scale networks, in terms of both computational efficiency and detected-community quality.

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