Summary of Similarity-navigated Conformal Prediction For Graph Neural Networks, by Jianqing Song et al.
Similarity-Navigated Conformal Prediction for Graph Neural Networks
by Jianqing Song, Jianguo Huang, Wenyu Jiang, Baoming Zhang, Shuangjie Li, Chongjun Wang
First submitted to arxiv on: 23 May 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 A recent paper in graph neural networks (GNNs) has made significant strides in semi-supervised node classification tasks, achieving remarkable accuracy. However, these results lack reliable uncertainty estimates, which is addressed by conformal prediction methods that provide a theoretical guarantee for node classification tasks, ensuring the conformal prediction set contains the ground-truth label with a desired probability (e.g., 95%). The authors propose a novel algorithm named Similarity-Navigated Adaptive Prediction Sets (SNAPS) that aggregates non-conformity scores based on feature similarity and structural neighborhood. SNAPS generates compact prediction sets, increases singleton hit ratio (correct prediction sets of size one), and maintains valid coverage, demonstrating its superiority in extensive experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make predictions more reliable when we don’t have a lot of information about what the data should look like. Right now, graph neural networks are really good at guessing what class something belongs to, but they don’t give us a sense of how sure they are. The authors come up with an idea called Similarity-Navigated Adaptive Prediction Sets (SNAPS) that looks at how similar different things are and uses that information to make better predictions. This helps them create smaller groups of correct predictions and makes the predictions more accurate. |
Keywords
» Artificial intelligence » Classification » Probability » Semi supervised
