Semantic-aware Heterogeneous Graph Attention Network Based on Multi-view RepresentationLearning

In recent years,graph neural networks have received widespread attention for their ability to efficiently process complex structures and rich semantic information in heterogeneous graphs.Learning low-dimensional node embeddings of heterogeneous graphs while preserving the heterogeneous structure and semantics for downstream tasks such as node classification and node clustering is a critical and challenging problem.Existing studies mainly design models based on meta-paths,but this approach faces at least two limitations.1)The selection of suitable meta-paths usually requires expert knowledge or additional labelling information.2)The approach restricts the model from learning by predefined patterns,which makes it difficult to adequately capture the complexity of the network.To address these issues,a multi-view and semantic-aware heterogeneous graph attention network(MS-HGANN) is proposed to merge nodes and relationships without manually designing meta-paths with the MS-HGANN consists of three main components:feature mapping,second-order view-specific self-graph fusion,and semantic aware.Feature mapping maps features to a uniform node feature space.Second-order view-specific self-graph fusion designs relationship-specific encoders and node attention to learn node representations on local structures.Semantic aware designs two coordinated attention mechanisms to evaluate the importance of nodes and relationships to obtain the final node representations.Experimental results on three publicly available datasets show that the proposed model is state-of-the-art for node classification and clustering tasks.