Summary of Conditional Distribution Learning on Graphs, by Jie Chen et al.
Conditional Distribution Learning on Graphs
by Jie Chen, Hua Mao, Yuanbiao Gou, Zhu Wang, Xi Peng
First submitted to arxiv on: 20 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper proposes a conditional distribution learning (CDL) method that learns graph representations from graph-structured data for semisupervised graph classification. The approach addresses the challenge of leveraging diverse and abundant data while preserving intrinsic semantic information. The authors present an end-to-end graph representation learning model that aligns the conditional distributions of weakly and strongly augmented features over the original features, enabling effective preservation of semantic information. To avoid conflict between message-passing mechanisms and contrastive learning, positive pairs are retained for measuring similarity between original and weakly augmented features. Experimental results on benchmark graph datasets demonstrate the effectiveness of the proposed method. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to learn graphs from data. Imagine you have lots of pictures of different objects, like cats and dogs. The goal is to teach a computer to recognize these objects without actually showing it all the pictures. This is called semisupervised learning. The researchers created a new method that helps the computer learn more about the objects by using different versions of the same picture. They tested this method on many pictures and found that it works really well! |
Keywords
» Artificial intelligence » Classification » Representation learning
