NAGNE: Node-to-Attribute Generation Network Embedding for Heterogeneous Network

Abstract

Heterogeneous network embedding aims to project multiple types of nodes into a low-dimensional space, and has become increasingly ubiquitous. However, several challenges have not been addressed so far. First, existing heterogeneous network embedding techniques typically rely on meta-paths to deal with the complex heterogeneous network. Using these meta-paths requires prior knowledge from domain experts for optimal meta-path selection. Second, few existing models can effectively consider both heterogeneous structural information and heterogeneous node attribute information. Third, existing models preserve the structure information by considering the first- and/or the second-order proximities, which cannot capture long-range structural information. To address these limitations, we propose a novel attributed heterogeneous network embedding model referred to as Node-to-Attribute Generation Network Embedding (NAGNE). NAGNE comprises two major components, the attributed random walk which samples node sequences in attributed heterogeneous network, and the node-to-attribute generation which learns the mapping that translates each node sequence itself from the node sequence to the node attribute sequence. Extensive experiments on three heterogeneous network datasets demonstrate that NAGNE outperforms state-of-the-art baselines in various data mining tasks.