## Datasets

This project evaluates its framework using four real-world graph datasets from various domains. These datasets help demonstrate the effectiveness and fairness aspects of the proposed graph neural network approach.

The datasets used are:

* **Credit Defaulter**: A financial graph where nodes represent customers and edges indicate similar credit behavior. This dataset is used to predict credit default.
* **German Credit**: A credit dataset where individuals are nodes and edges are based on feature similarities. This dataset is also focused on credit risk assessment.
* **Pokec_z** and **Pokec_n**: Subgraphs from the Pokec social network platform. In these datasets, nodes represent users, and edges signify friendships. These are used to test the framework on social network structures.

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