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https://github.com/dmlc/gnnlens2
Visualization tool for Graph Neural Networks
https://github.com/dmlc/gnnlens2
deep-learning dgl explainability graph-neural-networks graph-representation-learning graph-visualization pytorch visualization xai
Last synced: 2 days ago
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Visualization tool for Graph Neural Networks
- Host: GitHub
- URL: https://github.com/dmlc/gnnlens2
- Owner: dmlc
- License: apache-2.0
- Created: 2021-08-30T10:00:29.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-09-20T06:40:00.000Z (over 2 years ago)
- Last Synced: 2025-02-13T06:12:57.675Z (2 days ago)
- Topics: deep-learning, dgl, explainability, graph-neural-networks, graph-representation-learning, graph-visualization, pytorch, visualization, xai
- Language: TypeScript
- Homepage:
- Size: 20.9 MB
- Stars: 243
- Watchers: 11
- Forks: 27
- Open Issues: 5
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
GNNLens2 is an interactive visualization tool for graph neural networks (GNN). It allows seamless integration with [deep graph library (DGL)](https://github.com/dmlc/dgl) and can meet your various visualization requirements for presentation, analysis and model explanation. It is an open source version of [GNNLens](https://arxiv.org/abs/2011.11048) with simplification and extension.
A **video demo** is available [here](https://www.youtube.com/watch?v=eBI_lyzsg3M). Switch the video quality for the best viewing experience.
## Installation
### Requirements
- [PyTorch](https://pytorch.org/)
- [DGL](https://www.dgl.ai/pages/start.html)
- Flask-CORS
You can install Flask-CORS with
```bash
pip install -U flask-cors
```
### Installation for the latest stable version
```bash
pip install Flask==2.0.3
pip install gnnlens
```
### Installation from source
If you want to try experimental features, you can install from source as follows:
```bash
git clone https://github.com/dmlc/GNNLens2.git
cd GNNLens2/python
python setup.py install
```
### Verifying successful installation
Once you have installed the package, you can verify the success of installation with
```python
import gnnlens
print(gnnlens.__version__)
# 0.1.0
```
## Tutorials
We provide a set of tutorials to get you started with the library:
- [Tutorial 1: Graph structure](resources/tutorials/tutorial_1_graph.md)
- [Tutorial 2: Ground truth and predicted node labels](resources/tutorials/tutorial_2_nlabel.md)
- [Tutorial 3: Edge weights and attention](resources/tutorials/tutorial_3_eweight.md)
- [Tutorial 4: Weighted subgraphs and explanation methods](resources/tutorials/tutorial_4_subgraph.md)
## Team
**HKUST VisLab**: [Zhihua Jin](https://github.com/jnzhihuoo1), [Huamin Qu](http://huamin.org/)
**AWS Shanghai AI Lab**: [Mufei Li](https://github.com/mufeili), [Wanru Zhao](https://github.com/Ryan0v0) (work done during internship), [Jian Zhang](https://github.com/zhjwy9343), [Minjie Wang](https://jermainewang.github.io/)
**SMU**: [Yong Wang](http://yong-wang.org/)