Summary of Noisehgnn: Synthesized Similarity Graph-based Neural Network For Noised Heterogeneous Graph Representation Learning, by Xiong Zhang et al.
NoiseHGNN: Synthesized Similarity Graph-Based Neural Network For Noised Heterogeneous Graph Representation Learning
by Xiong Zhang, Cheng Xie, Haoran Duan, Beibei Yu
First submitted to arxiv on: 24 Dec 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 The proposed NoiseHGNN method tackles the challenge of noisy heterogeneous graph learning by synthesizing a similarity-based high-order graph and using a similarity-aware encoder to embed original and synthesized graphs. The approach synchronously supervises graph embeddings to predict the same labels, leveraging mutual information between metapaths extracted from both graphs. Extensive experiments on real-world datasets demonstrate state-of-the-art performance with improvements of +5% to +6% compared to previous methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem that happens when data about connections between things (like social media or websites) is noisy and mixed together. The noise makes it hard for machines to understand the data correctly. To fix this, researchers created a new way to look at the data using “similarity graphs” that help correct the mistakes. They also made a special kind of neural network that can work with both original and corrected data to make better predictions. The results show that this method is the best so far for solving this problem. |
Keywords
» Artificial intelligence » Encoder » Neural network
