Hierarchical Representation Learning for Attributed Networks

Abstract

Network representation learning, also called network embedding, aiming to learn low dimensional vectors for nodes while preserving essential properties of the network, benefits plenty of practical applications. However, how to do representation learning on the network quickly and effectively is a meaningful and challenging task, especially for the attributed networks. In this paper, we propose HANE, a Hierarchical Attributed Network Embedding framework, which is a fast and effective method by quickly constructing a hierarchical attributed network of different granularities to learn nodes representations. Specifically, for an attributed network, HANE first builds a hierarchy of successively smaller attributed network from fine to coarse by the fast granulation strategy fusing topological structure and node attributes. After using any unsupervised network embedding method to learn nodes representations of the coarsest network, HANE refines the nodes representations of the hierarchical attributed network from coarse to fine. HANE improves the speed of network representation learning while maintaining its performance and the representation learning method of the coarsest network is flexible. We conduct extensive evaluations for the proposed framework HANE on six datasets and two benchmark applications. Experimental results demonstrate that HANE achieves significant improvements over previous state-of-the-art network embedding methods in efficiency and effectiveness.