A New Relevance Measure for Heterogeneous Networks

Measuring relatedness between objects (nodes) in a heterogeneous network is a challenging and an interesting problem. Many people transform a heterogeneous network into a homogeneous network before applying a similarity measure. However, such transformation results in information loss as path semantics are lost. In this paper, we study the problem of measuring relatedness between objects in a heterogeneous network using only link information and propose a meta-path based novel measure for relevance measurement in a general heterogeneous network with a specified network schema. The proposed measure is semi-metric and incorporates the path semantics by following the specified meta-path. For relevance measurement, using the specified meta-path, the given heterogeneous network is converted into a bipartite network consisting only of source and target type objects between which relatedness is to be measured. In order to validate the effectiveness of the proposed measure, we compared its performance with existing relevance measures which are semi-metric and applicable to heterogeneous networks. To show the viability and the effectiveness of the proposed measure, experiments were performed on real world bibliographic dataset DBLP. Experimental results show that the proposed measure effectively measures the relatedness between objects in a heterogeneous network and it outperforms earlier measures in clustering and query task.