Hypergraph Partitioning for Community Discovery in Complex Network

Hypergraph partitioning has been considered as a promising method to address the challenges of high dimensionality in clustering. Many complex networks possess the scale-free property, which makes the task of community discobery from these networks difficult. In this paper we present a method of detecting community structure based on hypergraph model to address this problem. The hypergraph model maps the relationship in the original data into a hypergraph. A hyperedge represents a relationship among subsets of data and the weight of the hyperedge reflects the strength of this affinity. We assign the density of a hyperedge to its weight. We present and illustrate the results of experiments on the Enron data set. The experiments demonstrate that our approach is applicable and effective.