NANE: Attributed Network Embedding with Local and Global Information

Abstract

Attributed network embedding, which aims to map structural and attribute information into a latent vector space jointly, has attracted a surge of research attention in recent years. However, existing methods mostly concentrate on either the local proximity (i.e., the pairwise similarity of connected nodes) or the global proximity (e.g., the similarity of nodes’ correlation in a global perspective). How to learn the global and local information in structure and attribute into a same latent space simultaneously is an open yet challenging problem. To this end, we propose a Neural-based Attributed Network Embedding (NANE) approach. Firstly, an affinity matrix and an adjacency matrix are introduced to encode the attribute and structural information in terms of the overall picture separately. Then, we impose a neural-based framework with a pairwise constraint to learn the vector representation for each node. Specifically, an explicit loss function is designed to preserve the local and global similarity jointly. Empirically, we evaluate the performance of NANE through node classification and clustering tasks on three real-world datasets. Our method achieves significant performance compared with state-of-the-art baselines.