24 citations · 60 across the 11 of their papers we have counts for
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cs.LG2026
Unified Graph Prompt Learning via Low-Rank Graph Message Prompting
Beibei Wang, Bo Jiang, Ziyan Zhang +1
Graph Data Prompt (GDP), which introduces specific prompts in graph data for efficiently adapting pre-trained GNNs, has become a mainstream approach to graph fine-tuning learning p…
cs.LG2023
AGFormer: Efficient Graph Representation with Anchor-Graph Transformer
Bo Jiang, Fei Xu, Ziyan Zhang +2
To alleviate the local receptive issue of GCN, Transformers have been exploited to capture the long range dependences of nodes for graph data representation and learning. However,…