mlsanityData to 7 Oct 2026

Continuous-Depth Neural Models for Dynamic Graph Prediction

arXiv 2106.11581 ↗First posted 22 Jun 2021cs.LG cs.AI stat.MLcited by 7

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Abstract (arXiv)

We introduce the framework of continuous-depth graph neural networks (GNNs). Neural graph differential equations (Neural GDEs) are formalized as the counterpart to GNNs where the input-output relationship is determined by a continuum of GNN layers, blending discrete topological structures and differential equations. The proposed framework is shown to be compatible with static GNN models and is extended to dynamic and stochastic settings through hybrid dynamical system theory. Here, Neural GDEs improve performance by exploiting the underlying dynamics geometry, further introducing the ability to accommodate irregularly sampled data. Results prove the effectiveness of the proposed models across applications, such as traffic forecasting or prediction in genetic regulatory networks.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 46 of its 71 references that are in the map. First posted on arXiv 22 Jun 2021.

Builds on

46 of 71 references.

References · 46

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34 more
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Cited by year

Citing papers per year: 2021: 2; 2022: 1; 2023: 3; 2024: 1.

7 papers.

  1. 2023Adaptive Message Passing: A General Framework to Mitigate Oversmoothing, Oversquashing, and Underreachingcited by 23
  2. 2021Space-Time Graph Neural Networkscited by 10
  3. 2024Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equationscited by 8
  4. 2023Temporal Graph Neural Networks for Irregular Datacited by 7
  5. 2023Learning Dynamic Graph Embeddings with Neural Controlled Differential Equationscited by 7
  6. 2022Learning Interacting Dynamical Systems with Latent Gaussian Process ODEscited by 5
  7. 2021GOPHER: Categorical probabilistic forecasting with graph structure via local continuous-time dynamicscited by 1