Random Walk Based Hierarchical Collaborative Filtering for Directed Network Embedding

Abstract

The asymmetricsemantic information within directionality makes directed network embedding essentially different from undirected network embedding. Existing directed network embedding methods, explicitly or implicitly, are established by preserving pairwise interactions yet distinguishing in-direction and out-direction. Noticing that the inherent semantic connotation of edge directions is still seldom leveraged to enhance directed network embedding, in this work, we propose a novel random walk embedding framework named RW4CF to encode hierarchical collaborative filtering features within directed networks. In detail, we interpret the directed link as a type of user-item interaction and formulate the concept of hierarchical collaborative filtering matrices as a heuristic structural feature for directed network embedding. Specifically, we incorporate a collaborative filtering window in the random walk scheme to encode collaborative filtering information. Theoretical analysis is given to prove that the obtained embedding by RW4CF is indeed a factorization of the hierarchical collaborative filtering matrices of the directed network. Moreover, to validate the efficiency of RW4CF, we conduct extensive experiments on directed network datasets and demonstrate the superior performances of RW4CF than the state-of-the-arts.