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
© 2023 Fractal Analytics Inc. All rights reserved
04
Made with FlippingBook - PDF hosting