🚀 End-to-End Node and Graph Classification and Explanation App

This repo contains project code for the Graph Explainability system that when receives Node ID or Graph ID as user input:

• Classifies the node or graph;
• Displays feature importances based on computed explained feature mask;
• Shows Explanation Subgraph based on learned edge mask;
• Visualizes the original node (and its neighborhood) and graph based on which operation does the user wanna perform.

Requirements

• Python
• Streamlit (for app building and deployment)
• DVC (for data, model, and code version control)
• MLflow (to keep track of all graph representation learning experiments performed)
• Optuna (to find optimal values for all hyperparameters in exponentially large search space)
• PyTorch
• PyTorch Geometric

Data

• Node Classification: Cora dataset is used that contains a homogeneous graph comprising 2708 nodes and 1433 node_features along with 7 class labels.
• Graph Classification: ENZYMES dataset contains 600 homogeneous graphs along with 3 node_features and the task is to classify any of these graphs in 6 different Enzymes.

App Accessibility

To view and access the app, please click here or type in the following web address to your browser: https://graphexplanability.streamlit.app/

App Usage

To learn more about how to use this Graph Neural Networks-powered deployed app, please consider watching the following video:

Graph-Explainability-App.mp4