Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges

@article{Kim2022GraphAD,
  title={Graph Anomaly Detection With Graph Neural Networks: Current Status and Challenges},
  author={Hwan Kim and Byung Suk Lee and Won-Yong Shin and Sungsu Lim},
  journal={IEEE Access},
  year={2022},
  volume={10},
  pages={111820-111829}
}
Graphs are used widely to model complex systems, and detecting anomalies in a graph is an important task in the analysis of complex systems. Graph anomalies are patterns in a graph that do not conform to normal patterns expected of the attributes and/or structures of the graph. In recent years, graph neural networks (GNNs) have been studied extensively and have successfully performed difficult machine learning tasks in node classification, link prediction, and graph classification thanks to the… 

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