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arXiv · 1611.03254

When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks

Fan Zhang·Ying Zhang·Lu Qin·Wenjie Zhang·Xuemin Lin
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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 also consider the pairwise similarity between users based on their profiles. For a given similarity metric and a similarity threshold r, the similarity between any two vertices in a (k,r)-core is ensured not less than r. 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 significantly reduce the search space of two algorithms and a novel (k,k')-core based (k,r)-core size upper bound enhances performance of the maximum (k,r)-core computation. We also devise effective search orders to accommodate the different nature of two mining algorithms. Comprehensive experiments on real-life data demonstrate that the maximal/maximum (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of two mining algorithms is significantly improved by proposed techniques.

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Fan Zhang, Ying Zhang, Lu Qin, Wenjie Zhang, Xuemin Lin. 2016-11-10. When Engagement Meets Similarity: Efficient (k,r)-Core Computation on Social Networks. https://arxiv.org/abs/1611.03254

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