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http://dx.doi.org/10.3837/tiis.2018.07.005

K-Hop Community Search Based On Local Distance Dynamics  

Meng, Tao (College of Information Science and Engineering, Hunan University)
Cai, Lijun (College of Information Science and Engineering, Hunan University)
He, Tingqin (College of Information Science and Engineering, Hunan University)
Chen, Lei (College of Electrical and Information Engineering, Hunan University)
Deng, Ziyun (Department of Economics and Trade, ChangSha Commerce and Tourism College)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.12, no.7, 2018 , pp. 3041-3063 More about this Journal
Abstract
Community search aims at finding a meaningful community that contains the query node and also maximizes (minimizes) a goodness metric. This problem has recently drawn intense research interest. However, most metric-based algorithms tend to include irrelevant subgraphs in the identified community. Apart from 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 the k-hop and local distance dynamics model, which can naturally capture a community that contains the query node. The basic idea is to envision the nodes that k-hop away from the query node as an adaptive local dynamical system, where each node only interacts with its local topological structure. Relying on a proposed local distance dynamics model, the distances among nodes change over time, where the nodes sharing the same community with the query node tend to gradually move together, while other nodes stay far away from each other. Such interplay eventually leads to a steady distribution of distances, and a meaningful community is naturally found. Extensive experiments show that our community search algorithm has good performance relative to several state-of-the-art algorithms.
Keywords
Community Search; complex network; k-hop;
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