4 citations · 13 across the 15 of their papers we have counts for
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cs.LG2024
Node-Time Conditional Prompt Learning In Dynamic Graphs
Xingtong Yu, Zhenghao Liu, Xinming Zhang +1
Dynamic graphs capture evolving interactions between entities, such as in social networks, online learning platforms, and crowdsourcing projects. For dynamic graph modeling, dynami…
cs.LG2024★ 2 cited
Text-Free Multi-domain Graph Pre-training: Toward Graph Foundation Models
Xingtong Yu, Chang Zhou, Yuan Fang +1
Given the ubiquity of graph data, it is intriguing to ask: Is it possible to train a graph foundation model on a broad range of graph data across diverse domains? A major hurdle to…
cs.LG2024★ 4 cited
A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learning
Xingtong Yu, Yuan Fang, Zemin Liu +5
Graph representation learning, a critical step in graph-centric tasks, has seen significant advancements. Earlier techniques often operate in an end-to-end setting, which heavily r…