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


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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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).

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  • Clustering coefficient
  • Graph clustering
  • Combinatorial optimization