DPRel: A Meta-Path Based Relevance Measure for Mining Heterogeneous Networks

摘要

Mining a heterogeneous network requires measuring the relatedness between objects represented as nodes in the network. Relevance measurement on objects in a heterogeneous network is a challenging problem. Many researchers transform a heterogeneous network into the corresponding homogeneous network and then apply conventional similarity measures. However, this approach involves information loss as various path semantics are lost in the transformation process. In this paper, we study the problem of relevance measurement on objects in a heterogeneous network and propose a meta-path based semi-metric measure for relevance measurement on objects in a general heterogeneous network with a specified network schema. The proposed measure incorporates path semantics by following the specified meta-path. For measuring relatedness between objects using the proposed measure, the heterogeneous network is converted into a bipartite network consisting of source and target type objects following the specified meta-path. To validate the effectiveness of the proposed measure, we compare its performance with the existing meta-path based semi-metric measures applicable to heterogeneous networks. Experiments are performed on real-world datasets to show the effectiveness of the proposed measure.