Corpus ID: 211532662

Semi-supervised Anomaly Detection on Attributed Graphs

@article{Kumagai2020SemisupervisedAD,
  title={Semi-supervised Anomaly Detection on Attributed Graphs},
  author={A. Kumagai and Tomoharu Iwata and Y. Fujiwara},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.12011}
}
We propose a simple yet effective method for detecting anomalous instances on an attribute graph with label information of a small number of instances. Although with standard anomaly detection methods it is usually assumed that instances are independent and identically distributed, in many real-world applications, instances are often explicitly connected with each other, resulting in so-called attributed graphs. The proposed method embeds nodes (instances) on the attributed graph in the latent… Expand
3 Citations
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