Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network
This work addresses a limitation in heterogeneous graph embedding for researchers and practitioners in graph machine learning, though it is incremental as it builds on existing hyperbolic space methods.
The paper tackles the problem of capturing diverse power-law structures in heterogeneous graphs by proposing MSGAT, which uses multiple hyperbolic spaces instead of a single one, and it outperforms state-of-the-art baselines in various graph machine learning tasks.
To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, which aligns with the structural properties of heterogeneous graphs. However, despite heterogeneous graphs inherently possessing diverse power-law structures, most hyperbolic heterogeneous graph embedding models use a single hyperbolic space for the entire heterogeneous graph, which may not effectively capture the diverse power-law structures within the heterogeneous graph. To address this limitation, we propose Multi-hyperbolic Space-based heterogeneous Graph Attention Network (MSGAT), which uses multiple hyperbolic spaces to effectively capture diverse power-law structures within heterogeneous graphs. We conduct comprehensive experiments to evaluate the effectiveness of MSGAT. The experimental results demonstrate that MSGAT outperforms state-of-the-art baselines in various graph machine learning tasks, effectively capturing the complex structures of heterogeneous graphs.