K-Hop Community Search Based on Local Distance Dynamics
Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric, which has attracted a lot of attention in recent years. However, most of existing metric-based algorithms either tend to include the irrelevant subgraphs in the identified community or have computational bottleneck. Contrary to the user-defined metric algorithm, how can we search the natural community that the query node belongs to? In this paper, we propose a novel community search algorithm based on the concept of k-hop and local distance dynamics model, which can natural capture a community that contains the query node. Extensive experiments on large real-world networks with ground-truth demonstrate the effectiveness and efficiency of our community search algorithm and has good performance compared to state-of-the-art algorithm.
KeywordsCommunity search Interaction model Complex network
This work was supported by the National Natural Science Foundation of China (61174140, 61472127, 61272395); China Postdoctoral Science Foundation (2013M540628, 2014T70767); Natural Science Foundation of Hunan Province (14JJ3107); Excellent Youth Scholars Project of Hunan Province (15B087).
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