2024/01/20 by Moshe Eliasof, Eldad Haber, Eliasof, Moshe +5 · 2 citations
Computer Science · Neuroscience · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #FOS: Computer and information sciences #Functional Brain Connectivity Studies #Machine Learning (cs.LG)
paper · pdf · doi:10.48550/arxiv.2401.11074
openalex publication_date 2024/01/20 · openalex created_date 2024/01/24 · openalex updated_date 2026/07/28
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.