Parameter-Free Structural Diversity Search

  • Jinbin HuangEmail author
  • Xin Huang
  • Yuanyuan Zhu
  • Jianliang Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11881)


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 \(\mathsf {discriminative}\) \(\mathsf {core}\), which automatically models various kinds of social contexts without parameter t. Leveraging on \(\mathsf {discriminative}\) \(\mathsf {cores}\) 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. Extensive experiment results demonstrate the parameter sensitivity of existing t-core based model and verify the superiority of our methods.



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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jinbin Huang
    • 1
    Email author
  • Xin Huang
    • 1
  • Yuanyuan Zhu
    • 2
  • Jianliang Xu
    • 1
  1. 1.Hong Kong Baptist UniversityKowloon TongHong Kong
  2. 2.Wuhan UniversityWuhanChina

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