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20192026
most citedSpiking GATs: Learning Graph Attentions via Spiking Neural Network

2 citations · 5 across the 9 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.LG2026

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…