Lingjun Mao

BG-HGNN: Toward Efficient Learning for Complex Heterogeneous Graphs

2024 · arXiv (Cornell University)

Heterogeneous graphs, comprising diverse node and edge types connected through varied relations, are ubiquitous in real-world applications. Message-passing heterogeneous graph neural networks (HGNNs) have emerged as a powerful model class for such data. However, existing …