Summary of Fast Graph Sharpness-aware Minimization For Enhancing and Accelerating Few-shot Node Classification, by Yihong Luo et al.
Fast Graph Sharpness-Aware Minimization for Enhancing and Accelerating Few-Shot Node Classification
by Yihong Luo, Yuhan Chen, Siya Qiu, Yiwei Wang, Chen Zhang, Yan Zhou, Xiaochun Cao, Jing Tang
First submitted to arxiv on: 22 Oct 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 proposed integration of Sharpness-Aware Minimization (SAM) into Graph Neural Networks (GNNs) aims to enhance model generalization in Few-Shot Node Classification (FSNC). The standard SAM approach is computationally costly, so the authors introduce Fast Graph Sharpness-Aware Minimization (FGSAM), which combines GNNs for parameter perturbation and Multi-Layer Perceptrons (MLPs) for loss minimization. This method reutilizes gradients to incorporate graph topology at minimal additional cost. The FGSAM+ variant executes exact perturbations periodically, demonstrating faster optimization than the base optimizer in most cases. Experimental results show that the proposed algorithm outperforms standard SAM with lower computational costs in FSNC tasks and achieves competitive performance in standard node classification tasks for heterophilic graphs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are powerful tools for classifying nodes in graph-structured data. However, they struggle to make accurate predictions when only a few examples are available (few-shot learning). The authors propose a new way to train GNNs that combines two techniques: Sharpness-Aware Minimization and Multi-Layer Perceptrons. This method helps GNNs learn more general patterns from the data, making them better at predicting unseen classes with limited labels. |
Keywords
» Artificial intelligence » Classification » Few shot » Generalization » Optimization » Sam
