27 citations · 67 across the 23 of their papers we have counts for
6 papers · 1 filter
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…
Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generation
Wei Jin, Haitao Mao, Zheng Li +17
Modeling customer shopping intentions is a crucial task for e-commerce, as it directly impacts user experience and engagement. Thus, accurately understanding customer preferences i…
Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All?
Haitao Mao, Zhikai Chen, Wei Jin +5
Recent studies on Graph Neural Networks(GNNs) provide both empirical and theoretical evidence supporting their effectiveness in capturing structural patterns on both homophilic and…
Towards Label Position Bias in Graph Neural Networks
Haoyu Han, Xiaorui Liu, Feng Shi +3
Graph Neural Networks (GNNs) have emerged as a powerful tool for semi-supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming fr…
LazyGNN: Large-Scale Graph Neural Networks via Lazy Propagation
Rui Xue, Haoyu Han, MohamadAli Torkamani +2
Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting sca…