Summary of A Unified Graph Selective Prompt Learning For Graph Neural Networks, by Bo Jiang et al.
A Unified Graph Selective Prompt Learning for Graph Neural Networks
by Bo Jiang, Hao Wu, Ziyan Zhang, Beibei Wang, Jin Tang
First submitted to arxiv on: 15 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Graph Prompt Feature (GPF) has been successfully adapting pre-trained models for Graph Neural Networks (GNNs). However, existing GPFs have limitations in node prompt learning and ignoring edge prompting. To address these issues, a new unified Graph Selective Prompt Feature learning (GSPF) is proposed for GNN fine-tuning. This method integrates prompt learning on nodes and edges, providing a unified prompt model, and selectively learns prompts on important nodes and edges, making it more reliable and compact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graphs are special types of data that help computers understand relationships between things. There’s been a lot of research on using these graphs with special computer programs called Graph Neural Networks (GNNs). One way to make GNNs work better is by adding special “prompts” to the graph data. The problem is, most methods only focus on some parts of the graph and ignore others. To fix this, scientists have come up with a new idea: instead of just focusing on certain parts of the graph, they’re going to make the prompts work for all parts of the graph. This will help GNNs understand graphs better and make decisions that are more accurate. |
Keywords
» Artificial intelligence » Fine tuning » Gnn » Prompt » Prompting
