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arXiv · 1711.05857

An Optimal and Progressive Approach to Online Search of Top-k Influential Communities

Fei Bi·Lijun Chang·Xuemin Lin·Wenjie Zhang
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Abstract

Community search over large graphs is a fundamental problem in graph analysis. Recent studies propose to compute top-k influential communities, where each reported community not only is a cohesive subgraph but also has a high influence value. The existing approaches to the problem of top-k influential community search can be categorized as index-based algorithms and online search algorithms without indexes. The index-based algorithms, although being very efficient in conducting community searches, need to pre-compute a special-purpose index and only work for one built-in vertex weight vector. In this paper, we investigate on-line search approaches and propose an instance-optimal algorithm LocalSearch whose time complexity is linearly proportional to the size of the smallest subgraph that a correct algorithm needs to access without indexes. In addition, we also propose techniques to make LocalSearch progressively compute and report the communities in decreasing influence value order such that k does not need to be specified. Moreover, we extend our framework to the general case of top-k influential community search regarding other cohesiveness measures. Extensive empirical studies on real graphs demonstrate that our algorithms outperform the existing online search algorithms by several orders of magnitude.

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Fei Bi, Lijun Chang, Xuemin Lin, Wenjie Zhang. 2017-12-13. An Optimal and Progressive Approach to Online Search of Top-k Influential Communities. https://arxiv.org/abs/1711.05857

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