4 papers · 1 filter
CITE: A Comprehensive Benchmark for Heterogeneous Text-Attributed Graphs on Catalytic Materials
Chenghao Zhang, Qingqing Long, Ludi Wang +3
Text-attributed graphs(TAGs) are pervasive in real-world systems,where each node carries its own textual features. In many cases these graphs are inherently heterogeneous, containi…
Towards Graph Prompt Learning: A Survey and Beyond
Qingqing Long, Yuchen Yan, Peiyan Zhang +12
Large-scale "pre-train and prompt learning" paradigms have demonstrated remarkable adaptability, enabling broad applications across diverse domains such as question answering, imag…
A Survey of Data-Efficient Graph Learning
Wei Ju, Siyu Yi, Yifan Wang +4
Graph-structured data, prevalent in domains ranging from social networks to biochemical analysis, serve as the foundation for diverse real-world systems. While graph neural network…
Towards Graph Contrastive Learning: A Survey and Beyond
Wei Ju, Yifan Wang, Yifang Qin +10
In recent years, deep learning on graphs has achieved remarkable success in various domains. However, the reliance on annotated graph data remains a significant bottleneck due to i…