5 citations · 12 across the 13 of their papers we have counts for
6 papers · 1 filter
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,…
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…
Self-supervision meets kernel graph neural models: From architecture to augmentations
Jiawang Dan, Ruofan Wu, Yunpeng Liu +8
Graph representation learning has now become the de facto standard when handling graph-structured data, with the framework of message-passing graph neural networks (MPNN) being the…
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Sheng Tian, Jihai Dong, Jintang Li +7
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones. As instances that appear in the real world are naturally conne…
A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks
Jintang Li, Huizhe Zhang, Ruofan Wu +5
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensi…
DEDGAT: Dual Embedding of Directed Graph Attention Networks for Detecting Financial Risk
Jiafu Wu, Mufeng Yao, Dong Wu +6
Graph representation plays an important role in the field of financial risk control, where the relationship among users can be constructed in a graph manner. In practical scenarios…