Summary of Anomaly Detection in Graph Structured Data: a Survey, by Prabin B Lamichhane et al.
Anomaly Detection in Graph Structured Data: A Survey
by Prabin B Lamichhane, William Eberle
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 provides a comprehensive overview of anomaly detection techniques for processing complex real-world graphs. It discusses various application domains that utilize these techniques, including state-of-the-art methods categorized by assumptions and techniques. The authors highlight the advantages and disadvantages of current approaches and propose potential future research directions in graph-based anomaly detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to find unusual patterns in complex networks like social media or traffic systems. It’s important because these networks can be very big and hard to understand, but by finding anomalies we can improve things like safety and efficiency. The authors look at different ways to detect anomalies and group them into categories based on what they assume about the data and how they work. They also talk about what’s good and bad about each approach and suggest new areas for research. |
Keywords
» Artificial intelligence » Anomaly detection
