Unraveling Fraud Networks

How to implement a graph-based algorithm

There are three key steps to implementing graph-based algorithms to detect fraudulent activities.

GRAPH ANALYTICS AND MACHINE LEARNING

MODEL EVALUATION

DATA COLLECTION AND PRE-PROCESSING

START

Apply community detection algorithms to group nodes into clusters based on their connectivity patterns

Train the model on labeled data and evaluate its performance

Gather data on the entities to be analyzed

Compare model performance with baseline and basic graph features models

Construct a graph from the data, with nodes representing entities and edges representing relationships between them

Extract graph features and use them as inputs for machine learning models

Evaluate the model’s accuracy and variable importance to assess the impact of the graph features

Extract graph features, such as node degree, clustering coefficient, and centrality measures

Monitor and update the model as needed to ensure ongoing effectivenes

End

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