Using network analysis to identify Dutch companies at risk of violating Dutch labour laws: A comparison of a random forest method and a node classification method

In recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in an experiment of the Netherlands Labour Inspection to predict risk scores of violating Dutch labour laws for Dutch companies. In order to see if this is the best way for the NLA to use network analysis this method has been compared with using node classification, a deep learning method that uses sampling and aggregation to label nodes of a network based on its neighbours and the network structure. In this study node classification has shown to perform better at predicting risk scores for Dutch companies than a random forest model with network features does. A simulation study was done on the node classification method to test its robustness and has shown that it is important that there are enough labels in the training set in order for the method to perform well and that the quality of...Show moreIn recent years the use of network analysis has seen an increase across multiple fields. Using features derived from network analysis in a random forest model has already shown promising results in an experiment of the Netherlands Labour Inspection to predict risk scores of violating Dutch labour laws for Dutch companies. In order to see if this is the best way for the NLA to use network analysis this method has been compared with using node classification, a deep learning method that uses sampling and aggregation to label nodes of a network based on its neighbours and the network structure. In this study node classification has shown to perform better at predicting risk scores for Dutch companies than a random forest model with network features does. A simulation study was done on the node classification method to test its robustness and has shown that it is important that there are enough labels in the training set in order for the method to perform well and that the quality of these labels influences the extent to which the model overfits. If the data scientist of the NLA choose to use node classification in future projects it is important that they make an ethical selection of the variables to use for prediction and that they ensure that there are enough labels in the dataset for the node classification model to perform well.Show less

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Using network analysis to identify Dutch companies at risk of violating Dutch labour laws: A comparison of a random forest method and a node classification method