Papers › Random Walk-based Community Key-members Search over Large Graphs
Random Walk-based Community Key-members Search over Large Graphs
Yuxiang Wang, Yuyang Zhao, Xiaoliang Xu, Yue Wu, Tianxing Wu, Xiangyu Ke
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Given a graph G, a query node q, and an integer k, community search (CS) seeks a cohesive subgraph (measured by community models such as k-core or k-truss) from G that contains q. It is difficult for ordinary users with less knowledge of graphs' complexity to set an appropriate k. Even if we define quite a large k, the community size returned by CS is often too large for users to gain much insight about it. Compared against the entire community, key-members in the community appear more valuable than others. To contend with this, we focus on Community Key-members Search problem (CKS). We turn our perspective to the key-members in the community containing q instead of the entire community. To solve CKS problem, we first propose an exact algorithm based on truss decomposition as a baseline. Then, we present four random walk-based optimized algorithms to achieve a trade-off between effectiveness and efficiency, by carefully considering three important cohesiveness features in the design of transition matrix. As a result, we return key-members according to the stationary distribution when random walk converges. We theoretically analyze the rationality of designing the cohesiveness-aware transition matrix for random walk, through Bayesian theory based on Gaussian Mixture Model with Box-Cox Transformation and Copula Function Fitting. Moreover, we propose a lightweight refinement method following an ``expand-replace" manner to further optimize the result with little overhead, and we extend our method for CKS with multiple query nodes. Comprehensive experimental studies on various real-world datasets demonstrate our method's superiority.
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