On The Temporal Domain of Differential Equation Inspired Graph Neural Networks

• URL: http://arxiv.org/abs/2401.11074v1
• Date: Sat, 20 Jan 2024 01:12:57 GMT
• Title: On The Temporal Domain of Differential Equation Inspired Graph Neural Networks
• Authors: Moshe Eliasof, Eldad Haber, Eran Treister, Carola-Bibiane Sch\"onlieb
• Abstract summary: We show that our model, called TDE-GNN, can capture a wide range of temporal dynamics that go beyond typical first or second-order methods. We demonstrate the benefit of learning the temporal dependencies using our method rather than using pre-defined temporal dynamics on several graph benchmarks.
• Score: 14.779420473274737
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have demonstrated remarkable success in modeling complex relationships in graph-structured data. A recent innovation in this field is the family of Differential Equation-Inspired Graph Neural Networks (DE-GNNs), which leverage principles from continuous dynamical systems to model information flow on graphs with built-in properties such as feature smoothing or preservation. However, existing DE-GNNs rely on first or second-order temporal dependencies. In this paper, we propose a neural extension to those pre-defined temporal dependencies. We show that our model, called TDE-GNN, can capture a wide range of temporal dynamics that go beyond typical first or second-order methods, and provide use cases where existing temporal models are challenged. We demonstrate the benefit of learning the temporal dependencies using our method rather than using pre-defined temporal dynamics on several graph benchmarks.

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