3 citations · 3 across the 16 of their papers we have counts for
4 papers · 1 filter
Rethinking and Simplifying Bootstrapped Graph Latents
Wangbin Sun, Jintang Li, Liang Chen +3
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing m…
LasTGL: An Industrial Framework for Large-Scale Temporal Graph Learning
Jintang Li, Jiawang Dan, Ruofan Wu +9
Over the past few years, graph neural networks (GNNs) have become powerful and practical tools for learning on (static) graph-structure data. However, many real-world applications,…
The Devil is in the Data: Learning Fair Graph Neural Networks via Partial Knowledge Distillation
Yuchang Zhu, Jintang Li, Liang Chen +1
Graph neural networks (GNNs) are being increasingly used in many high-stakes tasks, and as a result, there is growing attention on their fairness recently. GNNs have been shown to…
HeteroNet: Heterophily-aware Representation Learning on Heterogenerous Graphs
Jintang Li, Zheng Wei, Jiawang Dan +9
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literat…