研究人员发表了对用于无线通信网络的图神经网络（GNN）的理论分析。该研究侧重于GNN在不同尺度上的可迁移性，特别是在稀疏网络环境中。通过检查随机几何图和确定性网格图之间的关系，该论文确定了尺度迁移过程中性能损失的界限。这些发现通过链路调度实验得到了验证，其中提出的GNN策略优于现有基准。 AI
影响 为将GNN应用于大规模无线网络提供了理论基础，有可能提高资源分配效率。
排序理由 该集群包含一篇在arXiv上发表的研究论文，详细介绍了图神经网络的理论分析。
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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-sc…
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 deployme…