Learning about data augmentation for graphs
In Chapter 8, Graph Analysis for Credit Card Transactions, we described how graph machine learning can be used to study and automatically detect fraudulent credit card transactions. While describing the use case, we faced two main obstacles:
- There were too many nodes in the original dataset to handle. As a consequence, the computational cost was too high to be computed. This is why we selected only 20% of the dataset.
- From the original dataset, we saw that less than 1% of the data had been labeled as fraudulent transactions, while the other 99% of the dataset contained genuine transactions. This is why, during the edge classification task, we randomly subsampled the dataset.
The techniques we used to solve these two obstacles, in general, are not optimal. For graph data, more complex and innovative techniques are needed to solve the task. Moreover, when datasets are highly unbalanced, as we mentioned in Chapter 8, Graph...
