A Method of Link Prediction Using Meta Path and Attribute Information

Abstract

Heterogeneous information networks (HIN) contain different types of nodes and edges. Predicting the connection between nodes in HIN is a non-trivial problem. Meta paths connect multiple types of nodes through a set of relationships, and are used to describe the different semantics of connections between different types of nodes in HIN. Although several similarity measures based on meta paths have been proposed, the challenge of how to use the measures under different paths to predict links remains open. Besides, the attribute information of nodes and edges in HIN can also be used for link prediction. In this paper, we propose a framework that combines similarity measures of meta path with other attribute information, and formulate a supervised learning task to find the optimal parameters. Experiments on a real data set show that the method has good performance in the problem of link prediction.