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Effective Resistance Based Weight Thresholding for Community Detection

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Artificial Life and Evolutionary Computation (WIVACE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1722))

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Abstract

Weight thresholding is often used as a sparsification procedure when graphs are too dense and reducing the number of edges is necessary to apply standard graph-theoretical methods. This work presents a proof-of-principle of a new evolutionary method based on Genetic Algorithms detecting communities in weighted networks by exploiting the concepts of effective resistance and weight thresholding. Given an input weighted graph, the algorithm considers its equivalent electric network where the edge weights are recomputed taking also into account the effective resistance, whose square root has shown to be a Euclidean metric. The method then generates a weighted sparse graph by maintaining for each node only the neighbors having weights below a given distance threshold. In such a way, only the neighbor nodes with low effective resistances and thus, highly similar according to this metric, are considered. Experiments on synthetically generated networks show that our approach is effective when compared to other benchmarks.

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Authors and Affiliations

  1. National Research Council of Italy (CNR), Institute for High Performance Computing and Networking (ICAR), Via Pietro Bucci, 8-9C, 87036, Rende, CS, Italy

    Clara Pizzuti & Annalisa Socievole

Authors
  1. Clara Pizzuti
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  2. Annalisa Socievole
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Correspondence to Annalisa Socievole .

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Editors and Affiliations

  1. Zurich University of Applied Sciences, Winterthur, Switzerland

    Johannes Josef Schneider

  2. School of Engineering, Cardiff University, Cardiff, UK

    Mathias Sebastian Weyland

  3. Zurich University of Applied Sciences, Winterthur, Switzerland

    Dandolo Flumini

  4. Zurich University of Applied Sciences, Winterthur, Switzerland

    Rudolf Marcel Füchslin

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Pizzuti, C., Socievole, A. (2022). Effective Resistance Based Weight Thresholding for Community Detection. In: Schneider, J.J., Weyland, M.S., Flumini, D., Füchslin, R.M. (eds) Artificial Life and Evolutionary Computation. WIVACE 2021. Communications in Computer and Information Science, vol 1722. Springer, Cham. https://doi.org/10.1007/978-3-031-23929-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-23929-8_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23928-1

  • Online ISBN: 978-3-031-23929-8

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