6 citations · 6 across the 3 of their papers we have counts for
3 papers
cs.LG2025
Graph Prompting for Graph Learning Models: Recent Advances and Future Directions
Xingbo Fu, Zehong Wang, Zihan Chen +7
Graph learning models have demonstrated great prowess in learning expressive representations from large-scale graph data in a wide variety of real-world scenarios. As a prevalent s…
cs.LG2024
Instance-Aware Graph Prompt Learning
Jiazheng Li, Jundong Li, Chuxu Zhang
Graph neural networks stand as the predominant technique for graph representation learning owing to their strong expressive power, yet the performance highly depends on the availab…
cs.SI2019★ 6 cited
Learning Individual Causal Effects from Networked Observational Data
Ruocheng Guo, Jundong Li, Huan Liu
Convenient access to observational data enables us to learn causal effects without randomized experiments. This research direction draws increasing attention in research areas such…