Relational neighbor classifier

A relation model is based on the idea that the behavior between nodes is correlated, meaning that connected nodes have a propensity to belong to the same class. The relational neighbor classifier, in particular, predicts a node's class based on its neighboring nodes and adjacent edges.

The dataset transfers consists of transactions from different accounts. The account_info data contains which of these accounts are money mules. However, it is unknown whether account "I41" is a money mule. Predict the money mule propensity of account "I41" using a relational neighbor classifier.

Create an undirected graph called net based on transfers. Set directed to the appropriate boolean (TRUE or FALSE).
Specify a color for each node: set V(net)$color to "darkorange" if account_info$isMoneyMule == TRUE and "slateblue1" otherwise.
Use subgraph() on net to create a subgraph called subset containing vertices "I41","I47", "I87" and "I20".
Use function strength() on subnet and on net to compute the money mule probability of node "I41" as the fraction of mule neighbors

Take Hint (-30 XP)

Exercise

Relational neighbor classifier

Instructions