Truss-based structural diversity search in large graphs (extended abstract)

Jinbin Huang, Xin Huang, Jianliang Xu

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

Abstract

Structural diversity, the multiplicity of social contexts inside an individual's contact neighborhood, is shown to play an important role in the social contagion process. Existing models have limited decomposability for analyzing large-scale networks, which may suffer from the inaccurate reflection of social context diversity. In this paper, we propose a truss-based structural diversity model to address the limitations. We study the problem of top-r structural diversity search to find r vertices with the largest truss-based structural diversity scores in a graph. We propose two novel index structures of TSD-index and GCT-index, and efficient index-based query processing algorithms to solve the problem. Extensive experiments demonstrate the effectiveness and efficiency of our proposed model and algorithms, against state-of-the-art methods.

Original languageEnglish
Title of host publicationProceedings - 2021 IEEE 37th International Conference on Data Engineering, ICDE 2021
PublisherIEEE Computer Society
Pages2346-2347
Number of pages2
ISBN (Electronic)9781728191843
ISBN (Print)9781728191850
DOIs
Publication statusPublished - Apr 2021
Event37th IEEE International Conference on Data Engineering, ICDE 2021 - Virtual, Chania, Greece
Duration: 19 Apr 202122 Apr 2021
https://ieeexplore.ieee.org/xpl/conhome/9458599/proceeding

Publication series

NameProceedings - International Conference on Data Engineering
Volume2021-April
ISSN (Print)1063-6382
ISSN (Electronic)2375-026X

Conference

Conference37th IEEE International Conference on Data Engineering, ICDE 2021
Country/TerritoryGreece
CityVirtual, Chania
Period19/04/2122/04/21
Internet address

Scopus Subject Areas

  • Software
  • Signal Processing
  • Information Systems

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