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Reign
Citation for published version (APA):
Pei, Y., Huang, T., van Ypenburg, W., & Pechenizkiy, M. (2022). Reign: attention based deep residual
modeling for anomaly detection on attributed networks.
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What is resgcn attention-based deep residual?
Resgcn attention-based deep residual is a type of graph convolutional network that incorporates attention mechanism and residual connections.
Who is required to file resgcn attention-based deep residual?
Researchers, developers, or organizations working in the field of graph neural networks are required to file resgcn attention-based deep residual.
How to fill out resgcn attention-based deep residual?
To fill out resgcn attention-based deep residual, one must provide detailed information about the model architecture, training process, and results obtained.
What is the purpose of resgcn attention-based deep residual?
The purpose of resgcn attention-based deep residual is to enhance the performance of graph neural networks by incorporating attention mechanism and residual connections.
What information must be reported on resgcn attention-based deep residual?
Information such as model architecture, hyperparameters, training data, evaluation metrics, and comparison with baseline models must be reported on resgcn attention-based deep residual.
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