{"type":"ScholarlyArticle","title":"Toward a Manifold-Preserving Temporal Graph Network in Hyperbolic Space","authors":[{"name":"Viet Quan Le","slug":"viet-quan-le","given":"Viet Quan","family":"Le","affiliation":""},{"name":"Viet Cuong Ta","slug":"viet-cuong-ta","given":"Viet-Cuong","family":"Ta","affiliation":""}],"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.","doi":"10.24963/ijcai.2024/484","url":"https://ijcai.org/proceedings/2024/484","pdf":"https://ijcai.org/proceedings/2024/0484.pdf","markdown":"https://ijcai.org/proceedings/2024/484.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/484","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":484,"pages":{"first":4380,"last":4388},"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"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}