Abstract
Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one hand, are influenced by the state of the source node and, on the other hand, cannot effectively identify the multiple communities linked with the overlapping nodes. We proposed a novel local community detection algorithm based on maximum clique extension called LCD-MC. The proposed method firstly finds the set of all the maximum cliques containing the source node and initializes them as the starting local communities; then, it extends each unclassified local community by greedy optimization until a certain objective is satisfied; finally, the expected local communities will be obtained until all maximum cliques are assigned into a community. An empirical evaluation using both synthetic and real datasets demonstrates that our algorithm has a superior performance to some of the state-of-the-art approaches.
Highlights
In recent years, more and more research has begun to pay attention to large complex networks, such as social networks, protein interaction networks, citation networks, and WWW
We introduce the proposed local community detection algorithm based on maximum cliques extension (LCD-MC), which is mainly composed of two parts, namely, algorithm FindMC for finding the maximum cliques of a node and algorithm LCD for local community extension corresponding to the maximum cliques
We propose a novel local community detection algorithm for large complex networks based on maximum cliques extension (LCD-MC)
Summary
More and more research has begun to pay attention to large complex networks, such as social networks, protein interaction networks, citation networks, and WWW. Extensive researches have indicated that community structure universally exists in complex networks and the connection between nodes in a community is closer than that between communities. These nodes often have similar attributes or play a similar role. Different from the global community detection which classifies a total complex network, local community detection is only to inquire the community structure where a designated node (source node) is located in a network. Local community detection need not know all information about a complex network in advance It starts from a node, gradually extends from the node, and gradually acquires the local information around the current community during the extension process. (iii) The experimental results on both synthetic and real networks demonstrate that, compared with the stateof-the-art local community detection algorithms, LCD-MC, on one side, can obtain better local community quality and, on the other side, can effectively identify multiple local community structures connected with the overlapped node
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