Graph Neural Networks Gone Hogwild

• URL: http://arxiv.org/abs/2407.00494v1
• Date: Sat, 29 Jun 2024 17:11:09 GMT
• Title: Graph Neural Networks Gone Hogwild
• Authors: Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams,
• Abstract summary: Message passing graph neural networks (GNNs) generate catastrophically incorrect predictions when nodes update asynchronously during inference. In this work we identify "implicitly-defined" GNNs as a class of architectures which is provably robust to partially asynchronous "hogwild" inference. We then propose a novel implicitly-defined GNN architecture, which we call an energy GNN.
• Score: 14.665528337423249
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Message passing graph neural networks (GNNs) would appear to be powerful tools to learn distributed algorithms via gradient descent, but generate catastrophically incorrect predictions when nodes update asynchronously during inference. This failure under asynchrony effectively excludes these architectures from many potential applications, such as learning local communication policies between resource-constrained agents in, e.g., robotic swarms or sensor networks. In this work we explore why this failure occurs in common GNN architectures, and identify "implicitly-defined" GNNs as a class of architectures which is provably robust to partially asynchronous "hogwild" inference, adapting convergence guarantees from work in asynchronous and distributed optimization, e.g., Bertsekas (1982); Niu et al. (2011). We then propose a novel implicitly-defined GNN architecture, which we call an energy GNN. We show that this architecture outperforms other GNNs from this class on a variety of synthetic tasks inspired by multi-agent systems, and achieves competitive performance on real-world datasets.

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