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Efficient Computation of K-Edge Connected Components: An Empirical Analysis

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Modelling and Mining Networks (WAW 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14671))

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Abstract

Graphs play a pivotal role in representing complex relationships across various domains, such as social networks and bioinformatics. Key to many applications is the identification of communities or clusters within these graphs, with k-edge connected components emerging as an important method for finding well-connected communities. Although there exist other techniques such as k-plexes, k-cores, and k-trusses, they are known to have some limitations.

This study delves into four existing algorithms designed for computing maximal k-edge connected subgraphs. We conduct a thorough study of these algorithms to understand the strengths and weaknesses of each algorithm in detail and propose algorithmic refinements to optimize their performance. We provide a careful implementation of each of these algorithms, using which we analyze and compare their performance on graphs of varying sizes. Our work is the first to provide such a direct experimental comparison of these four methods. Finally, we also address an incorrect claim made in the literature about one of these algorithms.

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Notes

  1. 1.

    The full version of this paper is available here.

  2. 2.

    https://github.com/Haniehsadri/KECC.

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Author information

Authors and Affiliations

  1. University of Victoria, Victoria, BC, Canada

    Hanieh Sadri & Alex Thomo

  2. Santa Clara University, Santa Clara, CA, USA

    Venkatesh Srinivasan

Authors
  1. Hanieh Sadri
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  2. Venkatesh Srinivasan
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  3. Alex Thomo
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Corresponding author

Correspondence to Hanieh Sadri .

Editor information

Editors and Affiliations

  1. Tutte Institute for Mathematics and Computing, Ottawa, ON, Canada

    Megan Dewar

  2. SGH Warsaw School of Economics, Warsaw, Poland

    Bogumił Kamiński

  3. SGH Warsaw School of Economics, Warsaw, Poland

    Daniel Kaszyński

  4. SGH Warsaw School of Economics, Warsaw, Poland

    Łukasz Kraiński

  5. Toronto Metropolitan University, Toronto, ON, Canada

    Paweł Prałat

  6. Tutte Institute for Mathematics and Computing, Ottawa, ON, Canada

    François Théberge

  7. SGH Warsaw School of Economics, Warsaw, Poland

    Małgorzata Wrzosek

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Sadri, H., Srinivasan, V., Thomo, A. (2024). Efficient Computation of K-Edge Connected Components: An Empirical Analysis. In: Dewar, M., et al. Modelling and Mining Networks. WAW 2024. Lecture Notes in Computer Science, vol 14671. Springer, Cham. https://doi.org/10.1007/978-3-031-59205-8_6

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  • DOI: https://doi.org/10.1007/978-3-031-59205-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-59204-1

  • Online ISBN: 978-3-031-59205-8

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