Arachne: An Arkouda Package for Large-Scale Graph Analytics

Oliver Alvarado Rodriguez, Zhihui Du, Joseph Patchett, Fuhuan Li, David A. Bader

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

4 Scopus citations

Abstract

Due to the emergence of massive real-world graphs, whose sizes may extend to terabytes, new tools must be developed to enable data scientists to handle such graphs efficiently. These graphs may include social networks, computer networks, and genomes. In this paper, we propose a novel graph package Arachne to make large-scale graph analytics more effortless and more efficient based on the open-source Arkouda framework, which has been developed to allow users to perform massively parallel computations on distributed data with an interface similar to NumPy. In this package, we developed a fundamental sparse graph data structure and several useful graph algorithms around our data structure to build a basic algorithmic library. Benchmarks and tools have also been developed to evaluate and demonstrate the provided graph algorithms. The graph algorithms we have implemented thus far include breadth-first search (BFS), connected components (CC), k-Truss (KT), Jaccard coefficients (JC), triangle counting (TC), and triangle centrality (TCE). Their corresponding experimental results based on realworld and synthetic graphs are presented.

Original languageEnglish (US)
Title of host publication2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665497862
DOIs
StatePublished - 2022
Event2022 IEEE High Performance Extreme Computing Conference, HPEC 2022 - Virtual, Online, United States
Duration: Sep 19 2022Sep 23 2022

Publication series

Name2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Volume2022-January

Conference

Conference2022 IEEE High Performance Extreme Computing Conference, HPEC 2022
Country/TerritoryUnited States
CityVirtual, Online
Period9/19/229/23/22

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Software
  • Computational Mathematics
  • Numerical Analysis

Keywords

  • graph analytics
  • large-scale data
  • open-source framework
  • parallel algorithm

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