Topology-Based Spectral Sparsification

Amyra Meidiana, Seok Hee Hong, Jiajun Huang, Peter Eades, Kwan Liu Ma

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

Abstract

Graph sampling is often used to reduce a large graph, with challenges to ensure the sample is representative of the original graph. Spectral sparsification is a related concept that creates a sparsified version of graphs that preserves the spectrum of the original graph.We present TSS, a sampling method combining spectral sparsification with topology-based decomposition of graphs. TSS aims to improve the runtime efficiency of spectral sparsification-based sampling through a Divide-and-Conquer approach using topology-based decomposition and combine it with the superior sampling quality spectral sparsification-based sampling offers over stochastic sampling. We also present DTSS, the distributed version of TSS, aimed for further runtime gains over sequential TSS.Experiments verify that TSS produces samples of the same quality as spectral sparsification-based sampling while attaining significant runtime improvements of up to 60% on real world datasets. DTSS on 5 servers runs up to another 80% faster compared to TSS.

Original languageEnglish (US)
Title of host publication2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages73-82
Number of pages10
ISBN (Electronic)9781728126050
DOIs
StatePublished - Oct 2019
Event9th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2019 - Vancouver, Canada
Duration: Oct 21 2019 → …

Publication series

Name2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019

Conference

Conference9th IEEE Symposium on Large-Scale Data Analysis and Visualization, LDAV 2019
CountryCanada
CityVancouver
Period10/21/19 → …

Keywords

  • Empirical studies in visualization
  • Graph drawing
  • Human-centered computing
  • Visualization
  • Visualization techniques

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Computer Science Applications
  • Media Technology

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  • Cite this

    Meidiana, A., Hong, S. H., Huang, J., Eades, P., & Ma, K. L. (2019). Topology-Based Spectral Sparsification. In 2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019 (pp. 73-82). [8944358] (2019 IEEE 9th Symposium on Large Data Analysis and Visualization, LDAV 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LDAV48142.2019.8944358