6 papers · 1 filter
A Unified Graph Language Model for Multi-Domain Multi-Task Graph Alignment Instruction Tuning
Haibo Chen, Xin Wang, Jiaheng Chao +2
Leveraging Graph Neural Networks (GNNs) as graph encoders and aligning the resulting representations with Large Language Models (LLMs) through alignment instruction tuning has beco…
Agentic AIs Are the Missing Paradigm for Out-of-Distribution Generalization in Foundation Models
Xin Wang, Haibo Chen, Wenxuan Liu +1
Foundation models (FMs) are increasingly deployed in open-world settings where distribution shift is the rule rather than the exception. The out-of-distribution (OOD) phenomena the…
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…
Towards Multimodal Graph Large Language Model
Xin Wang, Zeyang Zhang, Linxin Xiao +3
Multi-modal graphs, which integrate diverse multi-modal features and relations, are ubiquitous in real-world applications. However, existing multi-modal graph learning methods are…
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…
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…