Summary of Goodat: Towards Test-time Graph Out-of-distribution Detection, by Luzhi Wang et al.
GOODAT: Towards Test-time Graph Out-of-Distribution Detection
by Luzhi Wang, Dongxiao He, He Zhang, Yixin Liu, Wenjie Wang, Shirui Pan, Di Jin, Tat-Seng Chua
First submitted to arxiv on: 10 Jan 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 introduces GOODAT, a novel method for detecting graph out-of-distribution (OOD) samples at test-time. Unlike previous approaches that rely on modifying GNNs or training separate models, GOODAT is a data-centric, unsupervised, and plug-and-play solution that operates independently of training data and architecture modifications. The method uses a lightweight graph masker to learn informative subgraphs from test samples, enabling the capture of distinct graph patterns between OOD and in-distribution (ID) samples. To optimize the graph masker, three unsupervised objective functions are designed based on the graph information bottleneck principle. Comprehensive evaluations confirm that GOODAT outperforms state-of-the-art benchmarks across various real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about a new way to find when something doesn’t fit with the patterns we’ve learned from data, especially for complex data like graphs. When we use special kinds of computer models called graph neural networks (GNNs), they usually work well as long as the new data looks similar to what they were trained on. But sometimes, the new data is very different and these GNNs can make mistakes. The authors of this paper created a method called GOODAT that can identify when this happens and reject the wrong data. This means we can use our GNNs more safely and accurately. What’s special about GOODAT is that it doesn’t need to learn from all the old training data, which makes it faster and more efficient. |
Keywords
* Artificial intelligence * Unsupervised
