Summary of Anomix: a Simple Yet Effective Hard Negative Generation Via Mixing For Graph Anomaly Detection, by Hwan Kim et al.
ANOMIX: A Simple yet Effective Hard Negative Generation via Mixing for Graph Anomaly Detection
by Hwan Kim, Junghoon Kim, Sungsu Lim
First submitted to arxiv on: 27 Oct 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 proposes ANOMIX, a framework for graph anomaly detection (GAD) that leverages graph contrastive learning (GCL). To reduce the number of samples required by GCL, the authors introduce ANOMIX-M, a novel graph mixing approach. This method generates hard negatives by mixing abnormality and normality from input graphs, which is crucial for efficient GCL. The proposed framework consists of node- and subgraph-level contrasts to distinguish underlying anomalies. Experimental results show that ANOMIX achieves high accuracy (up to 5.49% higher) and efficiency (1.76% faster), reducing the number of samples required by nearly 80%. The code is available on GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Anomix is a new way to find unusual patterns in graphs. It uses a technique called graph contrastive learning, which helps machines learn from small amounts of data. The problem is that GCL often needs lots of data, but Anomix makes it work with much less data. This is because Anomix creates fake “hard negatives” that are similar to the unusual patterns we’re looking for. These hard negatives help machines learn faster and more accurately. Anomix also does two other things: it looks at individual nodes (like people) in the graph, and it looks at smaller groups of nodes (called subgraphs). This helps Anomix find even more unusual patterns. |
Keywords
» Artificial intelligence » Anomaly detection
