1 citations · 1 across the 10 of their papers we have counts for
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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…
When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning
Bo Jiang, Weijun Zhao, Beibei Wang +1
Graph Prompt Feature (GPF) learning has been widely used in adapting pre-trained GNN model on the downstream task. GPFs first introduce some prompt atoms and then learns the optima…
Robust Graph Fine-Tuning with Adversarial Graph Prompting
Ziyan Zhang, Bo Jiang, Jin Tang
Parameter-Efficient Fine-Tuning (PEFT) method has emerged as a dominant paradigm for adapting pre-trained GNN models to downstream tasks. However, existing PEFT methods usually exh…
Robust and Generalizable GNN Fine-Tuning via Uncertainty-aware Adapter Learning
Bo Jiang, Weijun Zhao, Beibei Wang +2
Recently, fine-tuning large-scale pre-trained GNNs has yielded remarkable attention in adapting pre-trained GNN models for downstream graph learning tasks. One representative fine-…