Summary

In this chapter, we described how a classical fraud detection task can be described as a graph problem and how the techniques described in the previous chapter can be used to tackle the problem. Going into more detail, we introduced the dataset we used and described the procedure to transform the transactional data into two types of graph, namely, bipartite and tripartite undirected graphs. We then computed local (along with their distributions) and global metrics for both graphs, comparing the results.

Moreover, a community detection algorithm was applied to the graphs in order to spot and plot specific regions of the transaction graph where the density of fraudulent transactions is higher compared to the other communities.

Finally, we solved the fraud detection problem using supervised and unsupervised algorithms, comparing the performances of the bipartite and tripartite graphs. As the first step, since the problem was unbalanced with a higher presence of genuine transactions...