26 citations · 82 across the 35 of their papers we have counts for
6 papers · 1 filter
Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization
Haonan Yuan, Qingyun Sun, Xingcheng Fu +4
Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribu…
Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
Zeyang Zhang, Xin Wang, Ziwei Zhang +2
Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to ha…
Disentangled Representation Learning with Large Language Models for Text-Attributed Graphs
Yijian Qin, Xin Wang, Ziwei Zhang +1
Text-attributed graphs (TAGs) are prevalent on the web and research over TAGs such as citation networks, e-commerce networks and social networks has attracted considerable attentio…
LLM4DyG: Can Large Language Models Solve Spatial-Temporal Problems on Dynamic Graphs?
Zeyang Zhang, Xin Wang, Ziwei Zhang +3
In an era marked by the increasing adoption of Large Language Models (LLMs) for various tasks, there is a growing focus on exploring LLMs' capabilities in handling web data, partic…
Graph Meets LLMs: Towards Large Graph Models
Ziwei Zhang, Haoyang Li, Zeyang Zhang +3
Large models have emerged as the most recent groundbreaking achievements in artificial intelligence, and particularly machine learning. However, when it comes to graphs, large mode…
Adversarially Robust Neural Architecture Search for Graph Neural Networks
Beini Xie, Heng Chang, Ziwei Zhang +5
Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk…