Translation-Based Attributed Network Embedding

Abstract

Attributed network embedding, which aims to map the structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, a vast majority of existing work explores the correlation between node structure and attribute values whereas the attribute type information which can be potentially complementary is ignored. How to effectively model the nodes, attribute types and attribute values as well as their relations in a unified framework is an open yet challenging problem. To this end, we propose a translation-based attributed network embedding method named TransANE. In our approach, the whole attributed network is considered as a coupled network which consists of two components, i.e., node relation network and attribute correlation network. We construct attribute correlation network by the co-occurrence of attribute values. Each node-attribute relation is regarded as an attributional triple, e.g., (Tom, Gender, Male). We introduce knowledge representation method to model the mapping between nodes, attribute types and attribute values. Empirically, experiments on two real-world datasets including node multi-class classification and network visualization are conducted to evaluate the effectiveness of our method TransANE in this paper. Our method achieves significant performance compared with state-of-the-art baselines.