7 papers
Long-Horizon Wireless Link Scheduling with State-Augmented Graph Neural Networks
Romina Garcia Camargo, Zhiyang Wang, Navid NaderiAlizadeh +1
We address optimal link scheduling in large-scale wireless networks. The goal is to schedule transmissions over a time horizon so that to maximize sum rate while ensuring that aver…
Limit Analysis of Graph Neural Networks with Wireless Conflict Graphs
Romina Garcia Camargo, Zhiyang Wang, Alejandro Ribeiro
Graph Neural Networks (GNNs) have emerged as a powerful tool for wireless resource allocation that leverages the underlying graph structure of communication networks. Their transfe…
Size Transferability of Graph Transformers with Convolutional Positional Encodings
Javier Porras-Valenzuela, Zhiyang Wang, Xiaotao Shang +2
Transformers have achieved remarkable success across domains, motivating the rise of Graph Transformers (GTs) as attention-based architectures for graph-structured data. A key desi…
Graph Neural Networks in Large Scale Wireless Communication Networks: Scalability Across Random Geometric Graphs
Romina Garcia Camargo, Zhiyang Wang, Alejandro Ribeiro
The growing complexity of wireless systems has accelerated the move from traditional methods to learning-based solutions. Graph Neural Networks (GNNs) are especially well-suited he…
Generalization of Geometric Graph Neural Networks with Lipschitz Loss Functions
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
In this paper, we study the generalization capabilities of geometric graph neural networks (GNNs). We consider GNNs over a geometric graph constructed from a finite set of randomly…
A Manifold Perspective on the Statistical Generalization of Graph Neural Networks
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Graph Neural Networks (GNNs) extend convolutional neural networks to operate on graphs. Despite their impressive performances in various graph learning tasks, the theoretical under…