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The VLDB Journal

, Volume 28, Issue 6, pp 987–1012 | Cite as

Efficient community discovery with user engagement and similarity

  • Fan ZhangEmail author
  • Xuemin Lin
  • Ying Zhang
  • Lu Qin
  • Wenjie Zhang
Regular Paper
  • 75 Downloads

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.

Keywords

Community detection User engagement User similarity Diversification 

Notes

Acknowledgements

Xuemin Lin is supported by 2018YFB1003504, NSFC61232006, ARC DP180103096 and DP170101628. Ying Zhang is supported by ARC DP180103096 and FT170100128. Lu Qin is supported by ARC DP160101513. Wenjie Zhang is supported by ARC DP180103096.

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Guangzhou UniversityGuangzhouChina
  2. 2.University of New South WalesSydneyAustralia
  3. 3.East China Normal UniversityShanghaiChina
  4. 4.Centre for AIUniversity of Technology SydneySydneyAustralia

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