Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network

• URL: http://arxiv.org/abs/2411.11283v1
• Date: Mon, 18 Nov 2024 04:55:26 GMT
• Title: Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network
• Authors: Jongmin Park, Seunghoon Han, Jong-Ryul Lee, Sungsu Lim,
• Abstract summary: We propose Multi-hyperbolic Space-based heterogeneous Graph Attention Network (MSGAT) to capture diverse power-law structures within heterogeneous graphs. MSGAT outperforms state-of-the-art baselines in various graph machine learning tasks.
• Score: 5.816451272912859
• License:
• Abstract: 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.

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