Summary of Rethinking Independent Cross-entropy Loss For Graph-structured Data, by Rui Miao et al.
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
by Rui Miao, Kaixiong Zhou, Yili Wang, Ninghao Liu, Ying Wang, Xin Wang
First submitted to arxiv on: 24 May 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 The proposed framework, termed joint-cluster supervised learning, enhances graph neural networks (GNNs) for node classification tasks. By modeling the joint distribution of each node with its corresponding cluster, the approach learns to condition on representations and extract data-label reference signals that strengthen discrimination ability. This is achieved by optimizing GNNs’ weights using a joint loss function. The framework’s effectiveness is demonstrated through extensive experiments, which show improved node classification accuracy and robustness against adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are super smart computers that can learn from complicated data. In this study, researchers found that traditional ways of training these GNNs didn’t work well when dealing with connected nodes. They came up with a new idea to teach the GNNs about clusters within the graph and use those clusters to help make better predictions. This new method was tested and showed that it can improve how well the GNNs do at classifying individual nodes, and also protect against fake or malicious data attacks. |
Keywords
» Artificial intelligence » Classification » Loss function » Supervised
