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Distributed Parallel Structural Hole Detection on Big Graphs

  • Faming Li
  • , Zhaonian Zou*
  • , Jianzhong Li
  • , Yingshu Li
  • , Yubiao Chen
  • *Corresponding author for this work
  • Harbin Institute of Technology
  • Georgia State University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Structural holes in social networks are vertices that serve as gateways for information exchange between communities. Although many algorithms have been proposed to detect structural holes, they are not scalable to big graphs. This paper proposes a structural hole detection algorithm ESH based on distributed parallel graph processing frameworks. Instead of using substructures in social networks, the algorithm exploits a factor diffusion process in structural hole detection. The algorithm naturally fits the vertex-centric programming models and can be easily implemented on the graph-parallel processing frameworks. Extensive experiments show that ESH can handle social networks with billions of links and produce structural holes of higher quality than the existing algorithms.

Original languageEnglish
Title of host publicationDatabase Systems for Advanced Applications - 24th International Conference, DASFAA 2019, Proceedings
EditorsGuoliang Li, Joao Gama, Yongxin Tong, Juggapong Natwichai, Jun Yang
PublisherSpringer Verlag
Pages519-535
Number of pages17
ISBN (Print)9783030185756
DOIs
StatePublished - 2019
Event24th International Conference on Database Systems for Advanced Applications, DASFAA 2019 - Chiang Mai, Thailand
Duration: 22 Apr 201925 Apr 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11446 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference24th International Conference on Database Systems for Advanced Applications, DASFAA 2019
Country/TerritoryThailand
CityChiang Mai
Period22/04/1925/04/19

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