Summary of Hg-adapter: Improving Pre-trained Heterogeneous Graph Neural Networks with Dual Adapters, by Yujie Mo and Runpeng Yu and Xiaofeng Zhu and Xinchao Wang
HG-Adapter: Improving Pre-Trained Heterogeneous Graph Neural Networks with Dual Adapters
by Yujie Mo, Runpeng Yu, Xiaofeng Zhu, Xinchao Wang
First submitted to arxiv on: 2 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a new approach to improving the performance of pre-trained heterogeneous graph neural networks (HGNNs) by addressing two limitations in current prompt-tuning-based methods. The first limitation is that the model may not fit the graph structures well, leading to decreased generalization ability, and the second is that the model may suffer from limited labeled data during the prompt-tuning stage, resulting in a large generalization gap. To alleviate these limitations, the authors derive a generalization error bound for existing prompt-tuning-based methods and propose a unified framework that combines two new adapters with potential labeled data extension to improve the generalization of pre-trained HGNN models. The proposed method includes dual structure-aware adapters that adaptively fit task-related homogeneous and heterogeneous structural information, as well as label-propagated contrastive loss and self-supervised losses to optimize the adapters and incorporate unlabeled nodes as potential labeled data. Theoretical analysis shows that the proposed method achieves a lower generalization error bound than existing methods, resulting in superior generalization ability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study aims to improve the performance of pre-trained graph neural networks by addressing two major limitations. Existing prompt-tuning-based methods may not be effective because they don’t take into account the complexity of graph structures and rely on limited labeled data. The researchers propose a new approach that combines two adapters with potential labeled data extension to adapt to different graph structures. They also design special losses to optimize the adapters and incorporate unlabeled nodes as potential labeled data. This method is expected to improve the generalization ability of pre-trained graph neural networks. |
Keywords
» Artificial intelligence » Contrastive loss » Generalization » Prompt » Self supervised
