Summary of Inductive Graph Alignment Prompt: Bridging the Gap Between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective, by Yuchen Yan et al.
Inductive Graph Alignment Prompt: Bridging the Gap between Graph Pre-training and Inductive Fine-tuning From Spectral Perspective
by Yuchen Yan, Peiyan Zhang, Zheng Fang, Qingqing Long
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 “Graph pre-training and fine-tuning” paradigm has been instrumental in improving Graph Neural Networks (GNNs) by capturing general knowledge without manual annotations for downstream tasks. However, a significant performance gap persists due to the vast difference between pre-training and fine-tuning stages. Inspired by prompt fine-tuning in Natural Language Processing (NLP), researchers have attempted to bridge this gap in the graph domain. Existing methods simply reformulate fine-tuning tasks to match pre-training ones, operating in transductive settings. To generalize graph pre-training to inductive scenarios where fine-tuning graphs may significantly differ from pre-training ones, we propose Inductive Graph Alignment Prompt (IGAP), a novel method that unifies mainstream graph pre-training frameworks and analyzes the essence of graph pre-training through graph spectral theory. IGAP bridges the data gap by learnable prompts in the spectral space, effectively addressing both graph signal and structure gaps. Our approach is theoretically sound and experimentally validated across nodes classification and graph classification tasks under transductive, semi-inductive, and inductive settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how to improve Graph Neural Networks (GNNs) by learning general knowledge without needing a lot of labeled data. Currently, this process has a big gap between the training stage and the testing stage. The authors take inspiration from Natural Language Processing (NLP) and develop a new way called Inductive Graph Alignment Prompt (IGAP). IGAP helps bridge this gap by finding patterns in the training data that can be applied to new, unseen data. This makes it possible to use pre-trained GNNs on different types of graphs without needing extra labeled data. |
Keywords
* Artificial intelligence * Alignment * Classification * Fine tuning * Natural language processing * Nlp * Prompt
