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Graph Clustering Via Intra-Cluster Density Maximization

  • Pierre MiasnikofEmail author
  • Leonidas Pitsoulis
  • Anthony J. Bonner
  • Yuri Lawryshyn
  • Panos M. Pardalos
Conference paper
  • 50 Downloads
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 315)

Abstract

Graph clustering, also often referred to as network community detection, is the process of assigning common labels to vertices that are densely connected to each other but sparsely connected to the rest of the graph. There are many different approaches to clustering in the literature. However, in this article, we formulate the clustering problem as a combinatorial optimization problem. Our main contribution is a novel problem formulation that maximizes intra-cluster density, a statistically meaningful quantity. It requires the number of clusters, a softbound on cluster size and a penalty coefficient as parameter inputs. More importantly, it is designed to prevent common degeneracies, like the so-called “mega-clusters”. We end with some suggestions on numerical solution techniques and note that an ensemble-like optimization routine seems promising.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Pierre Miasnikof
    • 1
    Email author
  • Leonidas Pitsoulis
    • 2
  • Anthony J. Bonner
    • 1
  • Yuri Lawryshyn
    • 1
  • Panos M. Pardalos
    • 3
    • 4
  1. 1.University of TorontoTorontoCanada
  2. 2.Aristotle University of ThessalonikiThessalonikiGreece
  3. 3.University of FloridaGainesvilleUSA
  4. 4.National Research University HSENizhny NovgorodRussian Federation

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