Parameter-Free Structural Diversity Search

Jinbin Huang*, Xin HUANG, Yuanyuan Zhu, Jianliang XU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)


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 which automatically models various kinds of social contexts without parameter t. Leveraging on 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.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering – WISE 2019 - 20th International Conference, Proceedings
EditorsReynold Cheng, Nikos Mamoulis, Yizhou Sun, Xin Huang
Number of pages17
ISBN (Print)9783030342227
Publication statusPublished - 29 Oct 2019
Event20th International Conference on Web Information Systems Engineering, WISE 2019 - , Hong Kong
Duration: 19 Jan 202022 Jan 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11881 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference20th International Conference on Web Information Systems Engineering, WISE 2019
Country/TerritoryHong Kong
Internet address

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)


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