76 citations · 115 across the 24 of their papers we have counts for
12 papers · 1 filter
Looped GNNs Beyond Fixed Depth: Graph Size Generalization and Parameter-Efficient Adaptation
Zhiyang Wang, Zhishang Luo, Yusu Wang
Generalizing from small graphs to larger ones can require computation beyond the depth of a neural model used during training. We study target tasks that admit a common local itera…
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 +3
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…
Generalization of Graph Neural Networks is Robust to Model Mismatch
Zhiyang Wang, Juan Cervino, Alejandro Ribeiro
Graph neural networks (GNNs) have demonstrated their effectiveness in various tasks supported by their generalization capabilities. However, the current analysis of GNN generalizat…
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…
Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the man…
Graphon Pooling for Reducing Dimensionality of Signals and Convolutional Operators on Graphs
Alejandro Parada-Mayorga, Zhiyang Wang, Alejandro Ribeiro
In this paper we propose a pooling approach for convolutional information processing on graphs relying on the theory of graphons and limits of dense graph sequences. We present thr…
Stability of Aggregation Graph Neural Networks
Alejandro Parada-Mayorga, Zhiyang Wang, Fernando Gama +1
In this paper we study the stability properties of aggregation graph neural networks (Agg-GNNs) considering perturbations of the underlying graph. An Agg-GNN is a hybrid architectu…
Stability to Deformations of Manifold Filters and Manifold Neural Networks
Zhiyang Wang, Luana Ruiz, Alejandro Ribeiro
The paper defines and studies manifold (M) convolutional filters and neural networks (NNs). \emph{Manifold} filters and MNNs are defined in terms of the Laplace-Beltrami operator e…
Graph and graphon neural network stability
Luana Ruiz, Zhiyang Wang, Alejandro Ribeiro
Graph neural networks (GNNs) are learning architectures that rely on knowledge of the graph structure to generate meaningful representations of large-scale network data. GNN stabil…
Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints
Cong Shen, Zhiyang Wang, Sofia S. Villar +1
Phase I dose-finding trials are increasingly challenging as the relationship between efficacy and toxicity of new compounds (or combination of them) becomes more complex. Despite t…
Regional Multi-Armed Bandits
Zhiyang Wang, Ruida Zhou, Cong Shen
We consider a variant of the classic multi-armed bandit problem where the expected reward of each arm is a function of an unknown parameter. The arms are divided into different gro…