Summary of Degnn: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise, by Tai Hasegawa et al.
DEGNN: Dual Experts Graph Neural Network Handling Both Edge and Node Feature Noise
by Tai Hasegawa, Sukwon Yun, Xin Liu, Yin Jun Phua, Tsuyoshi Murata
First submitted to arxiv on: 14 Apr 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 In this paper, researchers tackle the issue of noise in graph neural networks (GNNs), which can hinder their performance on real-world graph data. They propose a new GNN model called DEGNN, designed to mitigate noise in both edges and node features. The key idea is to use two separate experts that learn to modify edge and node representations through self-supervised learning. These modified representations are then used for downstream tasks, ensuring robustness against noise. The authors demonstrate the efficacy of DEGNN on various graph datasets with real-world and synthetic noise. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to understand a new kind of computer program that works with graphs. Graphs are like maps that show relationships between things. But sometimes, these maps can be messy or have errors. This makes it hard for the program to work well. The researchers in this paper want to make sure their graph program is good at handling mistakes in the map. They came up with a new way of doing this called DEGNN. It’s like having two experts working together: one that fixes the lines on the map (edges) and another that makes sure the places on the map have accurate information (node features). This helps the program work better even when the map is messy. |
Keywords
» Artificial intelligence » Gnn » Self supervised
