Computes metrics for 50 generated random walks using the function 'rw_client'.
Usage
mean_rw_client(v, g, data)
Value
A vector with the minimum, mean and maximum for both the number of steps and total transactioned amount in the random walks calculated.
Arguments
v
The initial vertex of the input graph.
g
The input graph. It should be a transactional graph with the amount as the attribute of each edge. The vertices must be the clients IDs.
data
Dataframe with information of the clients. It should include a column with the clients IDs named "customer_id" and the alert label named "sar_flag" that must be a boolean variable.
References
Eddin, A. N., Bono, J., Aparício, D., Polido, D., Ascensao, J. T., Bizarro, P., and Ribeiro, P. (2021). Anti-money laundering alert optimization using machine learning with graphs. arXiv preprint arXiv:2112.07508.