Tensorial graph learning for link prediction in generalized heterogeneous networks

Abstract

Tensorial graph learning frameworks are proposed for link predictions in heterogeneous, homogeneous and generalized heterogeneous networks. In these frameworks, tensorial graphs are used to represent different networks by incorporating node and edge tensors into the graphs. A tensorial graph kernel method is developed for link predictions in these networks using four types of, i.e., structural, behavioral, content and node/edge characteristics, data. In this method, a n-strand iterated algorithm and a tensorial graph based random walk algorithm are proposed to measure node similarities in different networks within the generalized heterogeneous networks, and a tensorial graph multi-kernel learning method is developed to integrate the results. Experimental results on two real-world social media databases show that the tensorial graph kernel method has better performance using all types of data than using one type of data alone or combinations of some types of data. The tensorial graph kernel method also performs considerably better than existing competitive methods.