A K-Truss Community Search Algorithm Based on Edge Enhancement
The goal of community search is to find cohesive and meaningful communities containing query nodes,which has attracted extensive research interest in recent years.Compared with low-order methods based on single nodes and edges,K-Truss community search methods aim to explore the high-order structure of complex networks.However,the query results usually have multiple subgraphs and isolated nodes.Therefore,this paper proposes a K-Truss community search method(BEMCS)based on edge enhancement to solve the fragmentation problem.It includes the following steps:first,the coarse subgraph containing the desired community is determined according to the query node.Then,the proximity between each node in the subgraph and query node it is measured,and these nodes are divided into several levels according to their proximity values,with lower level nodes having higher proximity values.Next,the edges are enhanced by connecting the lower-level nodes to their upper-level nodes.Finally,a K-Truss community search is performed on the enhanced subgraph to obtain the final community.Conducting a large number of experiments on various networks,experiments results shown that the method can effectively solve the fragmentation problem and perform better than some of the most advanced methods.
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