Papers › Graph Neural Reaction Diffusion Models
Graph Neural Reaction Diffusion Models
Moshe Eliasof, Eldad Haber, Eran Treister
The integration of Graph Neural Networks (GNNs) and Neural Ordinary and Partial Differential Equations has been extensively studied in recent years. GNN architectures powered by neural differential equations allow us to reason about their behavior, and develop GNNs with desired properties such as controlled smoothing or energy conservation. In this paper we take inspiration from Turing instabilities in a Reaction Diffusion (RD) system of partial differential equations, and propose a novel family of GNNs based on neural RD systems. We \textcolor{black}{demonstrate} that our RDGNN is powerful for the modeling of various data types, from homophilic, to heterophilic, and spatio-temporal datasets. We discuss the theoretical properties of our RDGNN, its implementation, and show that it improves or offers competitive performance to state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Node Classification | Actor | RDGNN-I | Accuracy | 38.69 ± 1.41 | #6 of 62 | Archive leaderboard | report |
| Node Classification | Chameleon | RDGNN-I | Accuracy | 74.79 ± 2.14 | #11 of 61 | Archive leaderboard | report |
| Node Classification | Cornell | RDGNN-I | Accuracy | 92.72 ± 5.88 | #1 of 60 | Archive leaderboard | report |
| Node Classification | Squirrel | RDGNN-I | Accuracy | 65.62 ± 2.33 | #16 of 59 | Archive leaderboard | report |
| Node Classification | Texas | RDGNN-S | Accuracy | 94.59 ± 5.97 | #1 of 62 | Archive leaderboard | report |
| Node Classification | Texas | RDGNN-I | Accuracy | 93.51 ± 5.93 | #2 of 62 | Archive leaderboard | report |
| Node Classification | Wisconsin | RDGNN-I | Accuracy | 93.72 ± 4.59 | #2 of 63 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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