Graph Neural Networks Gone Hogwild
Olga Solodova, Nick Richardson, Deniz Oktay, Ryan P. Adams
TL;DR
This paper introduces an energy-based GNN architecture that is robust to asynchronous inference, enabling reliable multi-agent system applications where synchrony cannot be guaranteed.
Contribution
The paper identifies implicitly-defined GNNs as robust to asynchrony and proposes a new energy GNN architecture with proven convergence guarantees.
Findings
Energy GNN outperforms other GNNs on synthetic multi-agent tasks.
Implicitly-defined GNNs are provably robust to asynchronous inference.
The approach extends GNN applicability to decentralized systems.
Abstract
Graph neural networks (GNNs) appear to be powerful tools to learn state representations for agents in distributed, decentralized multi-agent systems, but generate catastrophically incorrect predictions when nodes update asynchronously during inference. This failure under asynchrony effectively excludes these architectures from many potential applications where synchrony is difficult or impossible to enforce, e.g., robotic swarms or sensor networks. In this work we identify "implicitly-defined" GNNs as a class of architectures which is provably robust to asynchronous "hogwild" inference, adapting convergence guarantees from work in asynchronous and distributed optimization. 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…
Peer Reviews
Decision·ICLR 2025 Poster
- Although I am not deeply familiar with the literature on asynchronous inference in GNNs, the proposed Energy GNN model introduces a novel and meaningful contribution to this field. - The paper is of high quality. The authors provide comprehensive mathematical proofs for the convergence of Energy GNNs. They also provide a detailed description of the experimental setup and results, which are well-organized and easy to follow. - The paper is generally well-structured, with a clear definition of t
The paper lacks a clear and intuitive explanation of implicitly-defined GNNs, which is essential for understanding their robustness to asynchronous updates. While the authors offer detailed explanations for explicitly-defined GNNs, which are more straightforward, they do not provide the same depth of insight into implicitly-defined GNNs. This makes it difficult for readers unfamiliar with the topic to understand how implicitly-defined GNNs work and why they are resilient to asynchronous inferenc
**S1**. A theoretical study on the async inference with GNNs is timely and important - many real-world tasks are of that nature, so having a principled, robust approach for such problems (instead of tinkering standard sync GNN architectures) might be of interest to the graph learning community. **S2**. The paper is well-written - complex concepts are properly introduced and explained (which is often a challenge in the literature on implicit GNNs), the story and motivation are easy to follow.
The main problem of the work is in the experiments - it is hard to judge the claimed effectiveness of the proposed EnergyGNN using only synthetic experiments and basic GCN / GAT as baselines. **W1**. In the proposed suite of tasks, all implicit GNNs are robust in the async setup (the main goal of the work), and the main difference lies in the performance in the sync setup. Is there a different way to evaluate the differences among implicit GNNs other than on sync tasks? EnergyGNN is better than
By focusing on implicitly-defined GNNs, the work addresses a major limitation of conventional GNNs in handling asynchronous and unreliable communication, making it highly relevant for real-world multi-agent systems. Experimental results show that energy GNNs outperform other implicitly-defined GNNs on synthetic tasks, providing empirical validation for the architecture's effectiveness in multi-agent tasks (although most experiments are toy-ish.)
The experiments are conducted on toy examples. For the experiments other than the "terrain" examples there are great solutions that do not require machine learning. The results on the benchmark datasets in the supplementary show small improvements as compared to the more toy examples in the main manuscript.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsScientific Research and Philosophical Inquiry