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Temporal Graph Anomaly Detection

Creator
Creator
Seonglae ChoSeonglae Cho
Created
Created
2025 May 28 16:55
Editor
Editor
Seonglae ChoSeonglae Cho
Edited
Edited
2025 May 28 16:58
Refs
Refs
StrGNN
KnowledgeDiscovery • Updated 2025 Mar 19 9:22
 
 
 
 
 
 
 
Temporal graphs anomaly emergence detection: benchmarking for social media interactions
Applied Intelligence - Temporal graphs have become an essential tool for analyzing complex dynamic systems with multiple agents. Detecting anomalies in temporal graphs is crucial for various...
Temporal graphs anomaly emergence detection: benchmarking for social media interactions
https://link.springer.com/article/10.1007/s10489-024-05821-3
Temporal graphs anomaly emergence detection: benchmarking for social media interactions
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs | Proceedings of the 30th ACM International Conference on Information & Knowledge Management
The advantage of graph-based anomaly detection is that the relationships between elements can be analyzed for structural oddities that could represent activities such as fraud, network intrusions, or suspicious associations in a social network. ...
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs | Proceedings of the 30th ACM International Conference on Information & Knowledge Management
https://dl.acm.org/doi/10.1145/3459637.3481955
Structural Temporal Graph Neural Networks for Anomaly Detection in Dynamic Graphs | Proceedings of the 30th ACM International Conference on Information & Knowledge Management
arxiv.org
https://arxiv.org/pdf/2404.00060
 
 
 

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Temporal Graph Anomaly Detection
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