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Parallel algorithms for parameter-free structural diversity search on graphs

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Abstract

Structural diversity of a user in a social network is the number of social contexts in his/her contact neighborhood. The problem of structural diversity search is to find the top-k vertices with the largest structural diversity in a graph. However, when identifying distinct social contexts, existing structural diversity models (e.g., t-sized component, t-core, and t-brace) are sensitive to an input parameter of t. To address this drawback, we propose a parameter-free structural diversity model. Specifically, we propose a novel notation of discriminative core, which automatically models various kinds of social contexts without parameter t. Leveraging on discriminative cores and h-index, the structural diversity score for a vertex is calculated. We study the problem of parameter-free structural diversity search in this paper. An efficient top-k search algorithm with a well-designed upper bound for pruning is proposed. To further speed up the computation, we design a novel parallel algorithm for efficient top-k search over large graphs. The parallel algorithm computes diversity scores for a batch of vertices simultaneously using multi-threads. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.

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Acknowledgments

This work is supported by the NSFC Nos. 61702435, 61972291, RGC Nos. 12200917, 12200817, CRF C6030-18GF, and the National Science Foundation of Hubei Province No. 2018CFB519.

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Authors and Affiliations

  1. Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Jinbin Huang, Xin Huang & Jianliang Xu

  2. Wuhan University, Wuhan, China

    Yuanyuan Zhu

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  1. Jinbin Huang
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Correspondence to Xin Huang.

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This article belongs to the Topical Collection: Special Issue on Web Information Systems Engineering 2019

Guest Editors: Reynold Cheng, Nikos Mamoulis, and Xin Huang

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Huang, J., Huang, X., Zhu, Y. et al. Parallel algorithms for parameter-free structural diversity search on graphs. World Wide Web 24, 397–417 (2021). https://doi.org/10.1007/s11280-020-00843-6

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