Complex network analysis using parallel approximate motif counting

George M. Slota, Kamesh Madduri

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

14 Scopus citations

Abstract

Subgraph counting forms the basis of many complex network analysis metrics, including motif and anti-motif finding, relative graph let frequency distance, and graph let degree distribution agreements. Determining exact subgraph counts is computationally very expensive. In recent work, we present FASCIA, a shared-memory parallel algorithm and implementation for approximate subgraph counting. FASCIA uses a dynamic programming-based approach and is significantly faster than exhaustive enumeration, while generating high-quality approximations of subgraph counts. However, the memory usage of the dynamic programming step prohibits us from applying FASCIA to very large graphs. In this paper, we introduce a distributed-memory parallelization of FASCIA by partitioning the graph and the dynamic programming table. We discuss a new collective communication scheme to make the dynamic programming step memory-efficient. These optimizations enable scaling to much larger networks than before. We also present a simple parallelization strategy for distributed subgraph counting on smaller networks. The new additions let us use subgraph counts as graph signatures for a large network collection, and we analyze this collection using various subgraph count-based graph analytics.

Original languageEnglish (US)
Title of host publicationProceedings - IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS 2014
PublisherIEEE Computer Society
Pages405-414
Number of pages10
ISBN (Print)9780769552071
DOIs
StatePublished - Jan 1 2014
Event28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014 - Phoenix, AZ, United States
Duration: May 19 2014May 23 2014

Publication series

NameProceedings of the International Parallel and Distributed Processing Symposium, IPDPS
ISSN (Print)1530-2075
ISSN (Electronic)2332-1237

Other

Other28th IEEE International Parallel and Distributed Processing Symposium, IPDPS 2014
CountryUnited States
CityPhoenix, AZ
Period5/19/145/23/14

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Hardware and Architecture
  • Software

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    Slota, G. M., & Madduri, K. (2014). Complex network analysis using parallel approximate motif counting. In Proceedings - IEEE 28th International Parallel and Distributed Processing Symposium, IPDPS 2014 (pp. 405-414). [6877274] (Proceedings of the International Parallel and Distributed Processing Symposium, IPDPS). IEEE Computer Society. https://doi.org/10.1109/IPDPS.2014.50