Exploring Multiple Hypergraphs for Heterogeneous Graph Neural Networks

Graph neural networks have demonstrated significant power in learning graph representations for homogeneous networks. However, real-world network data can often be denoted by heterogeneous networks with different types of nodes and edges, such as social, traffic, and molecular networks. Network heterogeneity presents significant challenges for network analysis and mining. Motif-based hypergraphs preserve high-order proximity and capture composite semantic interactions. Because not all nodes and edges in the original network always exist in a specific hypergraph, it is essential that multiple motif-based hypergraphs are considered to enhance the network representation. Therefore, we propose a novel framework for exploring Multiple Motif-based Hypergraphs for Heterogeneous Graph Neural Networks to learn network representations, named MoH-HGNN, which leverages hypergraph convolution and attention operations to capture complex connectivity patterns. Specifically, we conducted two levels of attention networks with hierarchical structures, namely hyperedge-level attention to learn the importance among different types of nodes and comprehensive semantic-level attention to capture the importance of different types of motif structures. We extensively experimented on four real-world datasets to verify the effectiveness of our proposed framework.