Deep heterogeneous network embedding based on Siamese Neural Networks

Abstract

Heterogeneous network embedding aims at mapping a heterogeneous network into a low-dimensional latent space. There exist diverse relations among different types of objects in heterogeneous networks. However, most existing heterogeneous network embedding methods focus on exploring network structures instead of relations among different objects, so some redundant and fuzzy relations are inevitably captured. To address the problem, we propose a Relation-Oriented Deep Embedding (RODE) framework for heterogeneous networks that explores different relations among nodes. The captured relations are modeled through node similarity and dissimilarity. Based on the similarity and dissimilarity, a multi-task Siamese Neural Network is formulated to perform network embedding and optimize embedding representations. Extensive experiments are conducted on four heterogeneous networks. Experimental results demonstrate our method outperforms state-of-the-art embedding algorithms on several network mining tasks, such as link prediction, node classification and node clustering.