Summary of Anomaly Detection in Dynamic Graphs: a Comprehensive Survey, by Ocheme Anthony Ekle and William Eberle
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey
by Ocheme Anthony Ekle, William Eberle
First submitted to arxiv on: 31 May 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 Medium Difficulty summary: This paper presents a comprehensive survey on anomaly detection using dynamic graphs, focusing on existing graph-based techniques and their applications to dynamic networks. The authors review various approaches, including traditional machine-learning models, matrix transformations, probabilistic methods, and deep-learning approaches. They also discuss the advantages of graph-based techniques in capturing relational structures and complex interactions in dynamic graph data. The paper highlights current research trends, identifies open challenges, and provides a valuable resource for researchers and practitioners by summarizing the strengths and limitations of each approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about finding unusual patterns in changing networks. It looks at different ways to detect these anomalies using graphs that show how things are connected over time. The authors review many different approaches, from simple machine-learning models to more complex deep-learning methods. They explain why graph-based techniques are good for detecting patterns in dynamic networks and highlight the current research trends and challenges. |
Keywords
» Artificial intelligence » Anomaly detection » Deep learning » Machine learning
