Scalability of Graph Neural Network Policies in Wireless Communication Networks
A theoretical framework for GNN transferability over sparse Random Geometric Graphs (RGGs) is established, capturing distance-dependent channel decay and spatial interference and extending this framework to conflict graph models, deriving equivalent scalability guarantees for link-level resource allocation policies.