Connectivity-Based Spectral Sampling for Big Complex Network Visualization

Jingming Hu, Seok Hee Hong, Jialu Chen, Marnijati Torkel, Peter Eades, Kwan Liu Ma

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

Abstract

Graph sampling methods have been used to reduce the size and complexity of big complex networks for graph mining and visualization. However, existing graph sampling methods often fail to preserve the connectivity and important structures of the original graph. This paper introduces a new divide and conquer approach to spectral graph sampling based on the graph connectivity (i.e., decomposition of a connected graph into biconnected components) and spectral sparsification. Specifically, we present two methods, spectral vertex sampling and spectral edge sampling by computing effective resistance values of vertices and edges for each connected component. Experimental results demonstrate that our new connectivity-based spectral sampling approach is significantly faster than previous methods, while preserving the same sampling quality.

Original languageEnglish (US)
Title of host publicationComplex Networks and Their Applications IX - Volume 1, Proceedings of the Ninth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2020
EditorsRosa M. Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis Mateus Rocha, Marta Sales-Pardo
PublisherSpringer Science and Business Media Deutschland GmbH
Pages237-248
Number of pages12
ISBN (Print)9783030653460
DOIs
StatePublished - 2021
Event9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020 - Madrid, Spain
Duration: Dec 1 2020Dec 3 2020

Publication series

NameStudies in Computational Intelligence
Volume943
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503

Conference

Conference9th International Conference on Complex Networks and Their Application, COMPLEX NETWORKS 2020
CountrySpain
CityMadrid
Period12/1/2012/3/20

ASJC Scopus subject areas

  • Artificial Intelligence

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