12 citations · 24 across the 11 of their papers we have counts for
11 papers
Out-of-Distribution Generalization in Graph Foundation Models
Haoyang Li, Haibo Chen, Xin Wang +1
Graphs are a fundamental data structure for representing relational information in domains such as social networks, molecular systems, and knowledge graphs. However, graph learning…
Modular Machine Learning: An Indispensable Path towards New-Generation Large Language Models
Xin Wang, Haoyang Li, Haibo Chen +2
Large language models (LLMs) have substantially advanced machine learning research, including natural language processing, computer vision, data mining, etc., yet they still exhibi…
Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts
Zeyang Zhang, Xin Wang, Ziwei Zhang +5
Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution se…
Cross-Space Adaptive Filter: Integrating Graph Topology and Node Attributes for Alleviating the Over-smoothing Problem
Chen Huang, Haoyang Li, Yifan Zhang +2
The vanilla Graph Convolutional Network (GCN) uses a low-pass filter to extract low-frequency signals from graph topology, which may lead to the over-smoothing problem when GCN goe…
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…
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…
Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
Xin Wang, Ziwei Zhang, Haoyang Li +1
Graph machine learning has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and tec…
OOD-GNN: Out-of-Distribution Generalized Graph Neural Network
Haoyang Li, Xin Wang, Ziwei Zhang +1
Graph neural networks (GNNs) have achieved impressive performance when testing and training graph data come from identical distribution. However, existing GNNs lack out-of-distribu…
AutoGL: A Library for Automated Graph Learning
Ziwei Zhang, Yijian Qin, Zeyang Zhang +11
Recent years have witnessed an upsurge in research interests and applications of machine learning on graphs. However, manually designing the optimal machine learning algorithms for…
Noise Modulation: Let Your Model Interpret Itself
Haoyang Li, Xinggang Wang
Given the great success of Deep Neural Networks(DNNs) and the black-box nature of it,the interpretability of these models becomes an important issue.The majority of previous resear…