Summary of A Survey Of Graph Neural Networks in Real World: Imbalance, Noise, Privacy and Ood Challenges, by Wei Ju et al.
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges
by Wei Ju, Siyu Yi, Yifan Wang, Zhiping Xiao, Zhengyang Mao, Hourun Li, Yiyang Gu, Yifang Qin, Nan Yin, Senzhang Wang, Xinwang Liu, Xiao Luo, Philip S. Yu, Ming Zhang
First submitted to arxiv on: 7 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Social and Information Networks (cs.SI)
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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 paper presents a comprehensive survey on Graph Neural Networks (GNNs) that tackles four real-world challenges: data imbalance, noise, privacy protection, and out-of-distribution (OOD) scenarios. The authors highlight the significant performance degradation of GNN models due to these unfavorable factors in practical scenarios. They then review existing GNN models, focusing on solutions for each challenge, including methods for balancing data distributions, handling noisy data, protecting sensitive information, and generalizing to OOD scenarios. The survey aims to enhance the reliability and robustness of GNN models by exploring various approaches to address these challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary GNNs are a type of artificial intelligence that can learn from complex data structures like social networks or financial transactions. But when we try to use them in real-world situations, they often don’t work as well because the data is not perfect. The paper looks at four big problems with GNNs: unequal amounts of data, noisy or incorrect information, protecting sensitive data, and dealing with new situations that aren’t like the training data. The authors review different approaches to solving these challenges and show how they can make GNNs more reliable and robust. |
Keywords
* Artificial intelligence * Gnn
