Heterogeneous Information Network Embedding with Convolutional Graph Attention Networks

Heterogeneous Information Networks (HINs) are prevalent in our daily life, such as social networks and bibliography networks, which contain multiple types of nodes and links. Heterogeneous information network embedding is an effective HIN analysis method, it aims at projecting network elements into a lower-dimensional vector space for further machine learning related evaluations, such as node classification, node clustering, and so on. However, existing HIN embedding methods mainly focus on extracting the semantic-related information or close neighboring relations, while the high-level proximity of the network is also important but not preserved. To address the problem, in this paper we propose CGAT, a semi-supervised heterogeneous information network embedding method. We optimize the graph attention network by adding additional convolution layers, thereby we can extract multiple types of semantics and preserve high-level information in HIN embedding at the same time. Also, we utilize label information in HINs for semi-supervised training to better obtain the model parameters and HIN embeddings. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the proposed model.