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Spectral Vertex Sampling for Big Complex Graphs

  • Jingming Hu
  • Seok-Hee HongEmail author
  • Peter Eades
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 882)

Abstract

This paper introduces a new vertex sampling method for big complex graphs, based on the spectral sparsification, a technique to reduce the number of edges in a graph while retaining its structural properties. More specifically, our method reduces the number of vertices in a graph while retaining its structural properties, based on the high effective resistance values. Extensive experimental results using graph sampling quality metrics, visual comparison and shape-based metrics confirm that our new method significantly outperforms the random vertex sampling and the degree centrality based sampling.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer ScienceUniversity of SydneySydneyAustralia

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