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Efficient community discovery with user engagement and similarity
The VLDB Journal ( IF 1.973 ) Pub Date : 2019-10-26 , DOI: 10.1007/s00778-019-00579-4
Fan Zhang, Xuemin Lin, Ying Zhang, Lu Qin, Wenjie Zhang

Abstract In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we consider the pairwise similarity among users based on their attributes. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques substantially reduce the search space of two algorithms. A novel (\(k\),\(k'\))-core based (\(k\),\(r\))-core size upper bound enhances the performance of the maximum (k,r)-core computation. We also devise effective search orders for two algorithms with different search priorities for vertices. Besides, we study the diversified (\(k\),\(r\))-core search problem to find l maximal (\(k\),\(r\))-cores which cover the most vertices in total. These maximal (\(k\),\(r\))-cores are distinctive and informationally rich. An efficient algorithm is proposed with a guaranteed approximation ratio. We design a tight upper bound to prune unpromising partial (\(k\),\(r\))-cores. A new search order is designed to speed up the search. Initial candidates with large size are generated to further enhance the pruning power. Comprehensive experiments on real-life data demonstrate that the maximal (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of three mining algorithms is effectively improved by all the proposed techniques.
更新日期:2020-01-06

 

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