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Community detection on heterogeneous networks by multiple semantic-path clustering

Heterogeneous networks have become a commonly used model to represent complex and abstract social phenomena. They allow objects to have many different relationships and represent relationships by semantic paths which connect object types via a sequence of relations. A major challenge in community detection on heterogeneous networks is how to organize and combine different semantic paths. In order to acquire desired clustering, we propose a novel community detection method for heterogeneous networks based on matrix decomposition and semantic paths. The major advantage of this method is to treat objects individually and to assign them with different combinations of semantic-path weights so as to improve the clustering quality. The comparative experiments of the proposed method with another two state-of-the-art methods, spectral clustering and path-selection clustering, confirms that it can acquire desired clustering results better.