Summary of Higher-order Structure Based Anomaly Detection on Attributed Networks, by Xu Yuan et al.
Higher-order Structure Based Anomaly Detection on Attributed Networks
by Xu Yuan, Na Zhou, Shuo Yu, Huafei Huang, Zhikui Chen, Feng Xia
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 The paper presents a new anomaly detection method for attributed networks, called GUIDED, which utilizes higher-order network structures to model complex human behavior patterns. Existing graph learning methods lack an effective mechanism to apply these patterns, hindering anomaly detection progress. The proposed approach combines attribute autoencoder and structure autoencoder to reconstruct node attributes and higher-order structures, respectively. A graph attention layer evaluates the significance of neighbors based on their higher-order structure differences. Node attribute and higher-order structure reconstruction errors are used to detect anomalies. Experimental results on five real-world datasets (ACM, Citation, Cora, DBLP, and Pubmed) demonstrate GUIDED’s superiority over state-of-the-art methods in terms of ROC-AUC, PR-AUC, and Recall@K. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to detect unusual patterns in networks. Networks are made up of connections between things, like people or computers. Sometimes these connections can show us unusual behavior, like fraud or medical issues. The problem is that most current methods don’t take into account the complex relationships between these things. To fix this, the authors created a new method called GUIDED, which looks at both individual features and higher-level patterns in the network. They tested it on five real-world datasets and found that it works better than other existing methods. |
Keywords
» Artificial intelligence » Anomaly detection » Attention » Auc » Autoencoder » Recall
