Summary of Dcor: Anomaly Detection in Attributed Networks Via Dual Contrastive Learning Reconstruction, by Hossein Rafieizadeh et al.
DCOR: Anomaly Detection in Attributed Networks via Dual Contrastive Learning Reconstruction
by Hossein Rafieizadeh, Hadi Zare, Mohsen Ghassemi Parsa, Hadi Davardoust, Meshkat Shariat Bagheri
First submitted to arxiv on: 21 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 research paper proposes a novel anomaly detection method, DCOR, which leverages a network-based approach to identify abnormal events in various domains such as fraud detection and system fault diagnosis. The authors focus on attributed networks, where nodes have features and edges represent relationships between them. While earlier works mainly addressed predefined anomalies, this study integrates reconstruction-based anomaly detection with Contrastive Learning, utilizing a Graph Neural Network (GNN) framework. DCOR detects subtle anomalies by contrasting reconstructed adjacency and feature matrices from both the original and augmented graphs. Experimental studies on benchmark datasets demonstrate that DCOR significantly outperforms state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study introduces a new way to find unusual patterns in networks using a method called DCOR. Networks are used to represent complex systems, like social media or financial transactions. The goal is to identify unusual events, like fake accounts or fraudulent transactions. Most previous studies focused on finding predefined types of anomalies, but this one looks at the impact of data attributes and new types of anomalies that emerge over time. The authors use a special type of neural network called Graph Neural Networks (GNNs) to analyze networks and find these unusual patterns. By comparing original and altered versions of the networks, DCOR can detect subtle anomalies that other methods might miss. |
Keywords
» Artificial intelligence » Anomaly detection » Gnn » Graph neural network » Neural network
