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4 papers
A Pure Transformer Pretraining Framework on Text-attributed Graphs
Yu Song, Haitao Mao, Jiachen Xiao +6
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progr…
Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insights
Zhikai Chen, Haitao Mao, Jingzhe Liu +8
Given the ubiquity of graph data and its applications in diverse domains, building a Graph Foundation Model (GFM) that can work well across different graphs and tasks with a unifie…
Spectral-Aware Augmentation for Enhanced Graph Representation Learning
Kaiqi Yang, Haoyu Han, Wei Jin +1
Graph Contrastive Learning (GCL) has demonstrated remarkable effectiveness in learning representations on graphs in recent years. To generate ideal augmentation views, the augmenta…
Label-free Node Classification on Graphs with Large Language Models (LLMS)
Zhikai Chen, Haitao Mao, Hongzhi Wen +5
In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to…