https://github.com/fork123aniket/end-to-end-node-and-graph-classification-and-explanation-app

Streamlit App for Node and Graph Classification and Explainability
https://github.com/fork123aniket/end-to-end-node-and-graph-classification-and-explanation-app

cora dvc-for-data-science dvc-pipeline enzymes graph-classification graph-convolutional-networks graph-explain graph-neural-networks graph-representation-learning mlflow node-classificaiton optuna pytorch pytorch-geometric streamlit streamlit-cloud streamlit-sharing streamlit-webapp

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Streamlit App for Node and Graph Classification and Explainability

• Host: GitHub
• URL: https://github.com/fork123aniket/end-to-end-node-and-graph-classification-and-explanation-app
• Owner: fork123aniket
• License: mit
• Created: 2024-04-08T20:18:59.000Z (9 months ago)
• Default Branch: main
• Last Pushed: 2024-04-16T01:00:49.000Z (8 months ago)
• Last Synced: 2024-04-16T03:04:45.113Z (8 months ago)
• Topics: cora, dvc-for-data-science, dvc-pipeline, enzymes, graph-classification, graph-convolutional-networks, graph-explain, graph-neural-networks, graph-representation-learning, mlflow, node-classificaiton, optuna, pytorch, pytorch-geometric, streamlit, streamlit-cloud, streamlit-sharing, streamlit-webapp
• Language: HTML
• Homepage: https://graph-explanability.streamlit.app/
• Size: 3.23 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# :rocket: End-to-End Node and Graph Classification and Explanation App

[![Graph Explainability App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://graph-explanability.streamlit.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***](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.Planetoid.html#torch_geometric.datasets.Planetoid) dataset is used that contains a ***homogeneous graph*** comprising ***2708 nodes*** and ***1433 node_features*** along with ***7 class labels***.

- `Graph Classification`: [***ENZYMES***](https://pytorch-geometric.readthedocs.io/en/latest/generated/torch_geometric.datasets.TUDataset.html) 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***](https://graph-explanability.streamlit.app/) or type in the following web address to your browser:

[**https://graphexplanability.streamlit.app/**](https://graph-explanability.streamlit.app/)

## App Usage

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

https://github.com/fork123aniket/End-to-End-Node-and-Graph-Classification-and-Explanation-App/assets/92912434/7cf86648-2a76-4a7d-af8b-ad27b997b386