Summary of You Can’t Ignore Either: Unifying Structure and Feature Denoising For Robust Graph Learning, by Tianmeng Yang et al.
You Can’t Ignore Either: Unifying Structure and Feature Denoising for Robust Graph Learning
by Tianmeng Yang, Jiahao Meng, Min Zhou, Yaming Yang, Yujing Wang, Xiangtai Li, Yunhai Tong
First submitted to arxiv on: 1 Aug 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 recent study on the robustness of Graph Neural Networks (GNNs) under various noise and attack scenarios has significant implications for real-world applications. While previous research primarily focused on a single noise source, this paper addresses the more challenging scenario where both structures and features in graphs are compromised. The proposed Unified Graph Denoising (UGD) framework leverages high-order neighborhood proximity evaluation to identify noisy edges and simultaneously perturbed features. This is achieved through a graph auto-encoder-based reconstruction process and an iterative updating algorithm that optimizes the framework for robust graph learning. Our UGD framework is self-supervised, easy to implement as a plug-and-play module, and demonstrates superior performance in downstream tasks. Experimental results support the effectiveness of our method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers developed a new way to clean up noisy data in graphs that are used in machine learning models. Graphs can get messed up by errors or attacks, which makes it hard for models to learn from them correctly. This paper introduces a new approach called Unified Graph Denoising (UGD) that helps fix this problem. The UGD method uses two main steps: first, it identifies which parts of the graph are noisy and then it cleans those parts up using a special kind of auto-encoder. The method is easy to use and works well for different types of graphs. |
Keywords
» Artificial intelligence » Encoder » Machine learning » Self supervised
