Summary of Umgad: Unsupervised Multiplex Graph Anomaly Detection, by Xiang Li et al.
UMGAD: Unsupervised Multiplex Graph Anomaly Detection
by Xiang Li, Jianpeng Qi, Zhongying Zhao, Guanjie Zheng, Lei Cao, Junyu Dong, Yanwei Yu
First submitted to arxiv on: 19 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The proposed Unsupervised Multiplex Graph Anomaly Detection (UMGAD) method tackles two major challenges in graph anomaly detection: limited applicability to single-type interaction graphs and difficulty in selecting appropriate anomaly score thresholds in unsupervised scenarios. UMGAD learns multi-relational correlations among nodes in multiplex heterogeneous graphs, captures anomaly information through graph-masked autoencoder (GMAE), generates attribute-level and subgraph-level augmented-view graphs, and optimizes node attributes and structural features through contrastive learning. The approach is evaluated on four datasets, demonstrating average improvements of 13.48% in AUC and 11.68% in Macro-F1 compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph anomaly detection is important for identifying unusual patterns in complex networks. The new UMGAD method can find anomalies in graphs with different types of connections. It works by learning about the relationships between nodes, finding unusual node attributes and structures, and using this information to detect anomalies. The approach doesn’t need labeled data and can be used to detect fraud or identify unusual behavior on social media. |
Keywords
» Artificial intelligence » Anomaly detection » Auc » Autoencoder » Unsupervised
