Proceedings

Toward a Manifold-Preserving Temporal Graph Network in Hyperbolic Space

Viet Quan Le, Viet Cuong Ta

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Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence. Main Track. Pages 4380-4388.

https://doi.org/10.24963/ijcai.2024/484

Keywords

  • Machine Learning: ML: Geometric learning
  • Machine Learning: ML: Sequence and graph learning
  • Data Mining: DM: Mining spatial and/or temporal data
  • Data Mining: DM: Mining graphs

Abstract

Hyperbolic geometry provides an ideal setting to represent the scale-free or hierarchical characteristics of an input graph naturally. Utilizing hyperbolic geometry for learning dynamic graph representation has gained a growing interest in recent years. However, the majority of hyperbolic-based approaches rely on tangent spaces to perform graph operations, which could distort the structure of the dynamic graph when the graph grows over time. To avoid the distortion in tangent space, we propose a Hyperbolic Manifold-Preserving Temporal Graph Network that works directly on the hyperbolic manifold. The model includes a graph convolution module for learning the spatial dependencies, an attention architecture for capturing the temporal properties, and a gated recurrent unit for extracting the spatio-temporal relationships. By evaluating on diverse real-world dynamic graphs, our model has achieved significant improvements in link prediction and new link prediction tasks, in comparison with other baselines.

Viet Quan Le, and Viet-Cuong Ta. 2024. Toward a Manifold-Preserving Temporal Graph Network in Hyperbolic Space. In Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence, IJCAI-24. IJCAI, 4380–4388. https://doi.org/10.24963/ijcai.2024/484
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