Summary of Smoothgnn: Smoothing-aware Gnn For Unsupervised Node Anomaly Detection, by Xiangyu Dong et al.
SmoothGNN: Smoothing-aware GNN for Unsupervised Node Anomaly Detection
by Xiangyu Dong, Xingyi Zhang, Yanni Sun, Lei Chen, Mingxuan Yuan, Sibo Wang
First submitted to arxiv on: 27 May 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 abstract discusses a smoothing issue in graph learning that can uncover underlying properties of node anomaly detection (NAD). The authors introduce Individual Smoothing Patterns (ISP) and Neighborhood Smoothing Patterns (NSP), which indicate that anomalous nodes are harder to smooth than normal ones. They propose SmoothGNN, an unsupervised NAD framework that combines a learning component, spectral graph neural network, and coefficient design for effective node representation smoothing. The framework is evaluated on 9 real datasets, achieving a significant performance improvement of 14.66% in AUC and 7.28% in Average Precision, with a 75x speedup compared to rivals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper explores how a graph learning problem can actually help us detect anomalies in nodes better than before. It finds that when we try to smooth out node representations, anomalous nodes are harder to smooth than normal ones. This leads to the development of a new way to detect anomalies using “Individual Smoothing Patterns” and “Neighborhood Smoothing Patterns”. The authors test their idea with a special kind of neural network called SmoothGNN, which does really well on many real-world datasets. |
Keywords
» Artificial intelligence » Anomaly detection » Auc » Graph neural network » Neural network » Precision » Unsupervised
