Riemannian Liquid Spatio-Temporal Graph Network

• URL: http://arxiv.org/abs/2601.14115v1
• Date: Tue, 20 Jan 2026 16:09:05 GMT
• Title: Riemannian Liquid Spatio-Temporal Graph Network
• Authors: Liangsi Lu, Jingchao Wang, Zhaorong Dai, Hanqian Liu, Yang Shi,
• Abstract summary: Liquid-Constant networks (LTCs) excel at modeling irregularly-sampled dynamics but are fundamentally confined to Euclidean space.<n>This limitation introduces significant geometric distortion when representing real-world graphs with inherent non-Euclidean structures.<n>We introduce a framework that unifies continuous-time liquid dynamics with inductive inductive geometric biases.<n>RLSTG achieves superior performance on graphs with complex structures.
• Score: 6.583503277841693
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Liquid Time-Constant networks (LTCs), a type of continuous-time graph neural network, excel at modeling irregularly-sampled dynamics but are fundamentally confined to Euclidean space. This limitation introduces significant geometric distortion when representing real-world graphs with inherent non-Euclidean structures (e.g., hierarchies and cycles), degrading representation quality. To overcome this limitation, we introduce the Riemannian Liquid Spatio-Temporal Graph Network (RLSTG), a framework that unifies continuous-time liquid dynamics with the geometric inductive biases of Riemannian manifolds. RLSTG models graph evolution through an Ordinary Differential Equation (ODE) formulated directly on a curved manifold, enabling it to faithfully capture the intrinsic geometry of both structurally static and dynamic spatio-temporal graphs. Moreover, we provide rigorous theoretical guarantees for RLSTG, extending stability theorems of LTCs to the Riemannian domain and quantifying its expressive power via state trajectory analysis. Extensive experiments on real-world benchmarks demonstrate that, by combining advanced temporal dynamics with a Riemannian spatial representation, RLSTG achieves superior performance on graphs with complex structures. Project Page: https://rlstg.github.io

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