Summary of Hgoe: Hybrid External and Internal Graph Outlier Exposure For Graph Out-of-distribution Detection, by Junwei He et al.
HGOE: Hybrid External and Internal Graph Outlier Exposure for Graph Out-of-Distribution Detection
by Junwei He, Qianqian Xu, Yangbangyan Jiang, Zitai Wang, Yuchen Sun, Qingming Huang
First submitted to arxiv on: 31 Jul 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 researchers tackle the challenge of out-of-distribution (OOD) detection for graph data, which is critical in deep graph learning. They explore the use of auxiliary datasets to enhance OOD detection, a technique that has been studied extensively for image and text data but not yet applied to graph data. The proposed framework, called Hybrid External and Internal Graph Outlier Exposure (HGOE), combines realistic external graph data from various domains with synthesized internal outliers within ID subgroups to address the poor robustness of graph data. HGOE also includes a boundary-aware OE loss that assigns weights to outliers based on their quality. The framework is model-agnostic, designed to enhance the effectiveness of existing graph OOD detection models. Experimental results demonstrate significant performance improvements across 8 real datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to detect when data doesn’t fit into what we’re expecting. This is important for things like finding fake pictures or understanding how people are using social media. Right now, we don’t have a good way to do this with graph data, which is used in things like chemical bonding and social networks. The researchers suggest a new method called HGOE that uses real-world data from different places and also creates fake data that’s similar to what we’re trying to detect. This helps the computer understand what’s real and what’s not. They tested this on lots of real datasets and it worked really well. |
