Abstract
Detecting communities in complex networks has been one of the most popular research areas in recent years. There have been many community detection algorithms proposed to date. However, the local information (cliques) of communities and the search efficiency of algorithm have not been considered both in previous studies. In this paper, we propose a novel local expansion algorithm for detecting overlapping communities based on cliques. The algorithm draws on the assumption that cliques are the core of communities, as the clique takes into account the local characteristics of the community. The proposed algorithm adopts a single node with the maximum density as an initial community to prevent the formation of a large number of near-duplicate community structures, which improves the search efficiency of the algorithm. In many experiments using computer-generated and real-world networks, the proposed algorithm based on this idea verifies that the algorithm is able to detect overlapping communities effectively. The experiment yields better community uncover results, and the time efficiency and the complexity of algorithm are also satisfactory.
Highlights
Researchers are increasingly interested in the study of complex networks [1]
Fan: Local Optimization for Clique-Based Overlapping Community Detection attributes, humans belong to different groups
We propose a local optimization algorithm based on cliques (LOC) for overlapping community detection in complex networks
Summary
Researchers are increasingly interested in the study of complex networks [1]. They are typically used to represent complex systems, such as in society, biology, computer and other fields. Fan: Local Optimization for Clique-Based Overlapping Community Detection attributes, humans belong to different groups. We propose a local optimization algorithm based on cliques (LOC) for overlapping community detection in complex networks. The progress of clique-based locally optimized expansion can avoid repeated calculation of local optimization function and fully considered the local characteristics of a community when acquiring natural communities, initializing the community with a node rather than cliques avoids excessively near-duplicate, Different from previous work, the proposed algorithm greatly improves the efficiency of the algorithm and the quality of the community division. Many nodes may belong to multiple cliques, the LOC algorithm is able to detect overlapping community structures.
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