most citedAdversarial Robustness in Graph Neural Networks: A Hamiltonian Approach

9 citations · 18 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CL20243 cited

BANER: Boundary-Aware LLMs for Few-Shot Named Entity Recognition

Quanjiang Guo, Yihong Dong, Ling Tian +3

Despite the recent success of two-stage prototypical networks in few-shot named entity recognition (NER), challenges such as over/under-detected false spans in the span detection s…

cs.LG2023

SGA: A Graph Augmentation Method for Signed Graph Neural Networks

Zeyu Zhang, Shuyan Wan, Sijie Wang +5

Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hind…

cs.LG20239 cited

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…

cs.LG20233 cited

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…

cs.LG20231 cited

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…

cs.CV20232 cited

HypLiLoc: Towards Effective LiDAR Pose Regression with Hyperbolic Fusion

Sijie Wang, Qiyu Kang, Rui She +4

LiDAR relocalization plays a crucial role in many fields, including robotics, autonomous driving, and computer vision. LiDAR-based retrieval from a database typically incurs high c…