A GraphBLAS Implementation of Triangle Centrality

Fuhuan Li, David A. Bader

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Identifying key members in large social network graphs is an important graph analytic. Recently, a new centrality measure called triangle centrality finds members based on the triangle support of vertices in graph. In this paper, we describe our rapid implementation of triangle centrality using Graph-BLAS, an API specification for describing graph algorithms in the language of linear algebra. We use triangle centrality's algebraic algorithm and easily implement it using the SuiteSparse GraphBLAS library. A set of experiments on large, sparse graph datasets is conducted to verify the implementation.

Original languageEnglish (US)
Title of host publication2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665423694
DOIs
StatePublished - 2021
Event2021 IEEE High Performance Extreme Computing Conference, HPEC 2021 - Virtual, Online, United States
Duration: Sep 20 2021Sep 24 2021

Publication series

Name2021 IEEE High Performance Extreme Computing Conference, HPEC 2021

Conference

Conference2021 IEEE High Performance Extreme Computing Conference, HPEC 2021
Country/TerritoryUnited States
CityVirtual, Online
Period9/20/219/24/21

All Science Journal Classification (ASJC) codes

  • Modeling and Simulation
  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Computational Mathematics

Keywords

  • Graph algorithms
  • High Performance Data Analytics
  • Sparse matrix computations

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