GraphRay: Distributed pathfinder network scaling

Alessio Arleo, Oh Hyun Kwon, Kwan-Liu Ma

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

1 Scopus citations

Abstract

Pathfinder network scaling is a graph sparsification technique that has been popularly used due to its efficacy of extracting the 'important' structure of a graph. However, existing algorithms to compute the pathfinder network (PFNET) of a graph have prohibitively expensive time complexity for large graphs: O(n3) for the general case and O(n2 log n) for a specific parameter setting, PFNET(r = ∞, q = n - 1), which is considered in many applications. In this paper, we introduce the first distributed technique to compute the pathfinder network with the specific parameters (r = ∞ and q = n - 1) of a large graph with millions of edges. The results of our experiments show our technique is scalable; it efficiently utilizes a parallel distributed computing environment, reducing the running times as more processing units are added.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages74-83
Number of pages10
Volume2017-December
ISBN (Electronic)9781538606179
DOIs
StatePublished - Dec 19 2017
Event7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017 - Phoenix, United States
Duration: Oct 2 2017 → …

Other

Other7th IEEE Symposium on Large Data Analysis and Visualization, LDAV 2017
CountryUnited States
CityPhoenix
Period10/2/17 → …

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Media Technology
  • Library and Information Sciences

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  • Cite this

    Arleo, A., Kwon, O. H., & Ma, K-L. (2017). GraphRay: Distributed pathfinder network scaling. In 2017 IEEE 7th Symposium on Large Data Analysis and Visualization, LDAV 2017 (Vol. 2017-December, pp. 74-83). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV.2017.8231853