Summary of Reliable and Compact Graph Fine-tuning Via Graphsparse Prompting, by Bo Jiang et al.
Reliable and Compact Graph Fine-tuning via GraphSparse Prompting
by Bo Jiang, Hao Wu, Beibei Wang, Jin Tang, Bin Luo
First submitted to arxiv on: 29 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel approach to graph prompt learning, which adapts pre-trained Graph Neural Network (GNN) models for downstream graph learning tasks. The existing methods conduct prompting over all graph elements, but this is suboptimal and redundant. To address this issue, the authors propose Graph Sparse Prompting (GSP), which uses sparse representation theory to adaptively select optimal elements for compact prompting. Two types of GSP models are proposed: Graph Sparse Feature Prompting (GSFP) and Graph Sparse multi-Feature Prompting (GSmFP). These models can be used to tune any specific pre-trained GNNs, achieving attribute selection and compact prompt learning simultaneously. An algorithm is designed to solve the GSFP and GSmFP models. Experiments on 16 benchmark datasets validate the effectiveness of the proposed approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to help computers learn from graphs. Graphs are like maps that show connections between things. Right now, when we want computers to learn from these maps, we have to tell them which parts of the map to pay attention to. This can be slow and tricky. The authors suggest a better way: using special math called sparse representation theory. This lets computers pick just the right pieces of information from the graph, instead of looking at everything all at once. Two different ways to do this are proposed, both of which can help any computer learn more efficiently. |
Keywords
» Artificial intelligence » Attention » Gnn » Graph neural network » Prompt » Prompting
