35 citations · 94 across the 15 of their papers we have counts for
6 papers · 1 filter
Coupling Graph Neural Networks with Fractional Order Continuous Dynamics: A Robustness Study
Qiyu Kang, Kai Zhao, Yang Song +5
In this work, we rigorously investigate the robustness of graph neural fractional-order differential equation (FDE) models. This framework extends beyond traditional graph neural (…
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Kai Zhao, Qiyu Kang, Yang Song +3
Graph neural networks (GNNs) are vulnerable to adversarial perturbations, including those that affect both node features and graph topology. This paper investigates GNNs derived fr…
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
Qiyu Kang, Kai Zhao, Yang Song +2
In the graph node embedding problem, embedding spaces can vary significantly for different data types, leading to the need for different GNN model types. In this paper, we model th…
Graph Neural Convection-Diffusion with Heterophily
Kai Zhao, Qiyu Kang, Yang Song +3
Graph neural networks (GNNs) have shown promising results across various graph learning tasks, but they often assume homophily, which can result in poor performance on heterophilic…
Node Embedding from Hamiltonian Information Propagation in Graph Neural Networks
Qiyu Kang, Kai Zhao, Yang Song +3
Graph neural networks (GNNs) have achieved success in various inference tasks on graph-structured data. However, common challenges faced by many GNNs in the literature include the…
On the Robustness of Graph Neural Diffusion to Topology Perturbations
Yang Song, Qiyu Kang, Sijie Wang +2
Neural diffusion on graphs is a novel class of graph neural networks that has attracted increasing attention recently. The capability of graph neural partial differential equations…