Heterogeneous Graph Attention Network Based on Data Augmentation

Heterogeneous graph is a graph composed of different types of nodes and edges,which can model various types of objects and their relationships in the real world.Heterogeneous graph embedding aims to learn the embedding vectors of nodes by capturing rich attribute,structural and semantic information in the graph,which can be used in tasks such as node classification and link prediction,further achieving applications such as user recognition and product recommendation.The existing embedding methods exploit meta-paths to capture high-order structural and semantic information between nodes.However,the existing methods ignore the differences between different types of nodes in meta-path instances or different types of neighbor nodes in the graph,resulting in information loss,which in turn affects the quality of node embedding.In view of the above issues,this paper proposes heterogeneous graph attention network based on data augmentation(HANDA) to better learn the embedding vectors of nodes.Firstly,edge augmentation method based on meta-path neighbors is proposed.The method obtains neighbors of nodes based on meta-paths,and generates semantic edges between nodes and meta-path neighbors.These edges not only contain high-order structural and semantic information between nodes,but also alleviate the sparsity issue of the graph.Secondly,a node embedding method incorporating node type attention is presented.The method adopts the multi-head attention incorporating node types to obtain the importance of neighbors formed by both edges and semantic edges.Further,the method simultaneously captures attribute,high-order structural and semantic information by message passing and two kinds of neighbors,resulting in improving the embedding vectors of nodes.Experimental results on real datasets show that the proposed HANDA outperforms the baselines in both node classification and link prediction.