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DeGAN - Decomposition-based unified anomaly detection in static networks
Ahmet Tüzen
*
,
Yusuf Yaslan
*
Corresponding author for this work
Department of Computer Engineering
ASELSAN Inc.
Istanbul Technical University
Research output
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Contribution to journal
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Article
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peer-review
1
Citation (Scopus)
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Keyphrases
Decomposition Method
100%
Anomaly Detection
100%
Graph Anomaly Detection
100%
Static Networks
100%
Anomalous Nodes
50%
Graph Decomposition
50%
Detection Problem
25%
Learning Approaches
25%
Deep Learning
25%
Performance Improvement
25%
Detection Task
25%
Graph Neural Network
25%
Detecting Anomalies
25%
Anomaly Patterns
25%
Unified Solution
25%
Single Model
25%
Single Process
25%
Multiple Domains
25%
Single Framework
25%
Adversarial Learning
25%
Adversarial Autoencoder
25%
Graph Anomaly
25%
Learning Concepts
25%
Dynamic Graph
25%
Static Graph
25%
Attributed Graph
25%
Computer Science
Anomaly Detection
100%
Learning Approach
16%
Autoencoder
16%
Detect Anomaly
16%
Graph Neural Network
16%
Experimental Result
16%
Multiple Domain
16%
Deep Learning Method
16%