Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective 事件

Message Tuning Outshines Graph Prompt Tuning: A Prismatic Space Perspective arXiv:2606.03290v1 Announce Type: cross Abstract: Graph Foundation Models (GFMs), built upon the Pre-training and Adaptation paradigm, have emerged as a research hotspot in graph learning. For GNN-based GFMs, graph prompt tuning has become the prevailing adaptation method for downstream tasks. Although recent methods explain why graph prompt tuning works, how to rigorously measure its adaptation capacity remains an open