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Distributed Detection of Clusters of Arbitrary Size

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Structural Information and Communication Complexity (SIROCCO 2021)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12810))

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Abstract

Graph clustering is a fundamental technique in data analysis with a vast number of applications in computer science and statistics. In theoretical computer science, the problem of graph clustering has received significant research attention over the past two decades, which has led to pivotal algorithmic breakthroughs. However, the design of most graph clustering algorithms is based on complicated techniques from computational optimisation, which are not applicable for processing massive data sets stored in physically remote locations.

In this work we present a novel distributed algorithm for graph clustering. Most of the previous algorithms only work for graphs with balanced-sized clusters, which restrict their applications in many practical settings. Our proposed algorithm works for graphs with clusters of arbitrary size and its performance is analysed with respect to every individual cluster. In addition, our algorithm is easy to implement, and only requires a poly-logarithmic number of rounds for many graphs occurring in practice.

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Notes

  1. 1.

    Every node v can randomly select a number between \(\left[ 1, \mathrm {poly}(n)\right] \), such that, with high probability, those numbers are distinct.

  2. 2.

    The minimum does not play any special role here, it is only used to guarantee consensus among all nodes in the same cluster. The maximum ID works just as fine.

  3. 3.

    For a more detailed discussion of the connection between the sets \(\{f_i\}\), \(\{\chi _{S_i}\}\), \(\{ \widetilde{\chi }_i\}\) we refer the reader to [19].

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Acknowledgements

I would like to thank my supervisor Dr. He Sun for helpful discussion and comments on improving the presentation of this paper.

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Authors and Affiliations

  1. School of Informatics, The University of Edinburgh, Edinburgh, UK

    Bogdan-Adrian Manghiuc

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  1. Bogdan-Adrian Manghiuc
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Correspondence to Bogdan-Adrian Manghiuc .

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Editors and Affiliations

  1. Institute of Computer Science, University of Wrocław, Wroclaw, Poland

    Tomasz Jurdziński

  2. University of Vienna, Vienna, Austria

    Stefan Schmid

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Manghiuc, BA. (2021). Distributed Detection of Clusters of Arbitrary Size. In: Jurdziński, T., Schmid, S. (eds) Structural Information and Communication Complexity. SIROCCO 2021. Lecture Notes in Computer Science(), vol 12810. Springer, Cham. https://doi.org/10.1007/978-3-030-79527-6_21

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  • DOI: https://doi.org/10.1007/978-3-030-79527-6_21

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  • Online ISBN: 978-3-030-79527-6

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