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This research provides theoretical bounds on the transferability of Graph Neural Networks when applied to sparse wireless conflict graphs, demonstrating that models trained on small-scale instances generalize effectively to large-scale deployments with minimal performance loss. By validating these findings through link scheduling tasks, the study confirms that learned policies outperform existing benchmarks while clarifying the impact of geometric assumptions on empirical results. The work offers a rigorous foundation for deploying scalable GNNs in resource-constrained wireless environments where network topology varies significantly.
Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs
arXiv:2606.03794v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation that leverages the underlying graph structure of communication networks. Their transferability property enables models trained on small-scale graphs to generalize to large-scale deployments with little performance deterioration, a desirable property for currently growing networks. Wireless networks are sparse regimes, where a single node is connected to a small number of other users. This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs). In particular, we focus on conflict graphs of RGGs used to model interference among links. Our approach considers the closeness between RGGs and Deterministic Grid Graphs (DGG) to establish bounds in the performance loss when a model is transferred across scales. We validate our theoretical findings through the problem of link scheduling, demonstrating that our learned policies consistently outperform existing benchmarks at scale. Finally, we examine the impact of our theoretical assumptions on empirical performance.
Abstract:Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation that leverages the underlying graph structure of communication networks. Their transferability property enables models trained on small-scale graphs to generalize to large-scale deployments with little performance deterioration, a desirable property for currently growing networks. Wireless networks are sparse regimes, where a single node is connected to a small number of other users. This work establishes theoretical results for transferability of GNNs over graphs derived from sparse Random Geometric Graphs (RGGs). In particular, we focus on conflict graphs of RGGs used to model interference among links. Our approach considers the closeness between RGGs and Deterministic Grid Graphs (DGG) to establish bounds in the performance loss when a model is transferred across scales. We validate our theoretical findings through the problem of link scheduling, demonstrating that our learned policies consistently outperform existing benchmarks at scale. Finally, we examine the impact of our theoretical assumptions on empirical performance.
Machine Learning (cs.LG); Signal Processing (eess.SP)
arXiv:2606.03794 [cs.LG]
(or arXiv:2606.03794v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2606.03794 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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