Representation learning using Attention Network and CNN for Heterogeneous networks

Network embedding (NE), also known as network representation learning (NRL), is a method to learn a low-dimensional latent representation of nodes in an information network. The real-world data is usually presented in the form of heterogeneous information network (HIN) with multiple types of nodes and edges. Because of the rich information in HINs, it is necessary for a network embedding method to incorporate this information into the low-dimensional potential representation of the nodes as much as possible. In this paper, we propose a semi-supervised representation learning model using a graph attention network and a convolutional neural network (CNN) for HINs, called RANCH. In the part of the graph attention network, we construct a heterogeneous graph attention network using heterogeneous edges to preserve the features of nodes and the structure of network. In the part of the CNN, we leverage a 1D-CNN sentence classification model from natural language processing (NLP) community by adopting edge-constrained truncated random walks to generate node sequences, which can be treated as a corpus of words and sentences. The latter part further integrates the structural information of the network on the basis of the previous part and strengthens the influence of the node’s label information on the node representation. We have performed experiments of node classification on three real-world datasets, and the result shows that our model performs better than the state-of-the-arts. • A network embedding method for heterogeneous information network is proposed. • Most network embedding methods require the use of meta-paths for semantic learning. • The semantic information is learned by multi-typed edges without meta-paths here. • The embeddings of all types of nodes in the network are learned at the same time. • Our model performs better in node classification than most state-of-the-art methods.