9 papers
Position: How can Graphs Help Large Language Models?
Xiyuan Wang, Yi Hu, Yanbo Wang +2
With the rapid advancement of large language models (LLMs), classic graph learning tasks have greatly benefited from LLMs, including improved encoding of textual features, more eff…
Restoring Network Evolution from Static Structure
Jiu Zhang, Zhanwei Du, Hongwei Hu +6
The dynamical evolution of complex networks underpins the structure-function relationships in natural and artificial systems. Yet, restoring a network's formation from a single sta…
Bridging Code Graphs and Large Language Models for Better Code Understanding
Zeqi Chen, Zhaoyang Chu, Yi Gui +3
Large Language Models (LLMs) have demonstrated remarkable performance in code intelligence tasks such as code generation, summarization, and translation. However, their reliance on…
Large Language Model Enhanced Graph Invariant Contrastive Learning for Out-of-Distribution Recommendation
Jiahao Liang, Haoran Yang, Xiangyu Zhao +4
Out-of-distribution (OOD) generalization has emerged as a significant challenge in graph recommender systems. Traditional graph neural network algorithms often fail because they le…
Towards Text-free Graph Foundation Models: Rethinking Multi-Domain Graph Contrastive Learning
Zihao Zhao, Xinlong Zhai, Jinyu Yang +1
Foundation models have achieved great success in natural language processing (NLP) and computer vision (CV). Their success largely stems from the ability to integrate multi-domain…
Advancing Molecular Graph-Text Pre-training via Fine-grained Alignment
Yibo Li, Yuan Fang, Mengmei Zhang +1
Understanding molecular structure and related knowledge is crucial for scientific research. Recent studies integrate molecular graphs with their textual descriptions to enhance mol…