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A graph clustering algorithm based on a clustering coefficient for weighted graphs

Abstract

Graph clustering is an important issue for several applications associated with data analysis in graphs. However, the discovery of groups of highly connected nodes that can represent clusters is not an easy task. Many assumptions like the number of clusters and if the clusters are or not balanced, may need to be made before the application of a clustering algorithm. Moreover, without previous information regarding data label, there is no guarantee that the partition found by a clustering algorithm automatically extracts the relevant information present in the data. This paper proposes a new graph clustering algorithm that automatically defines the number of clusters based on a clustering tendency connectivity-based validation measure, also proposed in the paper. According to the computational results, the new algorithm is able to efficiently find graph clustering partitions for complete graphs.

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Correspondence to Mariá C. V. Nascimento.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Nascimento, M.C.V., Carvalho, A.C.P.L.F. A graph clustering algorithm based on a clustering coefficient for weighted graphs. J Braz Comput Soc 17, 19–29 (2011). https://doi.org/10.1007/s13173-010-0027-x

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Keywords

  • Clustering coefficient
  • Graph clustering
  • Combinatorial optimization