An Efficient Multi-View Heterogeneous Hypergraph Convolutional Network for Heterogeneous Information Network Representation Learning

Heterogeneous hypergraph neural networks are powerful tools to capture complex correlations among various nodes in Heterogeneous Information Networks (HINs). Despite satisfied performances of them, they are still plagued by the following problems: 1) They cannot capture the correlations in structural and semantic view at once, leading to topological information loss. 2) Due to the number of nodes being greater than the number of node types, node-level self-attention they used causes massive parameters and leads to high time consumption. 3) Interactions in meta-paths may be redundant, resulting in the correlations bias. To address the three issues, we propose an efficient <u>M</u>ulti-<u>V</u>iew <u>H</u>eterogeneous <u>H</u>yper<u>g</u>raph <u>C</u>onvolutional <u>N</u>etwork (MVH <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> GCN). It first constructs relational and semantic hypergraphs based on different types of edges and meta-paths respectively, to represent the complex correlations in structural view and semantic view. Meanwhile, the clean semantic hypergraphs are generated by structure learning network to avoid redundancy. Then, an efficient hypergraph convolutional network is designed to learn node embeddings. By doing so, correlations in the two views are captured. Finally, the learned node embeddings from two views are aggregated via a gated embedding fusion module for downstream tasks. Experiment results demonstrate that MVH <inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> GCN is effective and efficient.