18 citations · 32 across the 3 of their papers we have counts for
3 papers
cs.LG2024
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…
cs.IR2024★ 14 cited
GPT4Rec: Graph Prompt Tuning for Streaming Recommendation
Peiyan Zhang, Yuchen Yan, Xi Zhang +5
In the realm of personalized recommender systems, the challenge of adapting to evolving user preferences and the continuous influx of new users and items is paramount. Conventional…
cs.LG2024★ 18 cited
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
Yuchen Yan, Peiyan Zhang, Zheng Fang +1
The "Graph pre-training and fine-tuning" paradigm has significantly improved Graph Neural Networks(GNNs) by capturing general knowledge without manual annotations for downstream ta…