Summary of Gradient Rewiring For Editable Graph Neural Network Training, by Zhimeng Jiang et al.
Gradient Rewiring for Editable Graph Neural Network Training
by Zhimeng Jiang, Zirui Liu, Xiaotian Han, Qizhang Feng, Hongye Jin, Qiaoyu Tan, Kaixiong Zhou, Na Zou, Xia Hu
First submitted to arxiv on: 21 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 A novel approach to editable training of graph neural networks (GNNs) is presented, addressing the challenge of preserving performance on training nodes while correcting prediction errors. The proposed Gradient Rewiring method (GRE) first stores the anchor gradient of the loss on training nodes to preserve locality and then rewrites the gradient of the target node to ensure performance on training nodes using this anchor gradient. The effectiveness of GRE is demonstrated across various model architectures and graph datasets in multiple editing situations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are super powerful tools that help us understand complex data like social networks, traffic patterns, or biology. But sometimes these models can make mistakes when the world changes. To fix these mistakes, we need to update our GNNs using new information. This is called “model editing.” The problem is that GNNs are very good at understanding how different pieces of information relate to each other, which makes it hard to correct errors without affecting the model’s overall performance. In this paper, researchers propose a simple yet effective way to edit GNNs while preserving their original abilities. They show that their method works well in various situations and can be used with different types of models and data. |
