# INFORMATION GAIN PROPAGATION: A NEW WAY TO GRAPH ACTIVE LEARNING WITH SOFT LABELS.

This repository is the official implementation of IGP. 

## Requirements

To install requirements:

```setup
pip install -r requirements.txt
```


## Training

To train the model(s) in the paper:


> cd the  “example” data

>run the python file IGP.py 


## Results

1. Accuracy comparison:

<img src="accuracy.png" width="80%" height="80%">

2. GCN performance comparison in different budget:

<img src="gcn.png" width="80%" height="80%">

3. Ablation study:

<img src="ablation.png" width="80%" height="80%">

4. Generalization:

<img src="generalization.png" width="80%" height="80%">

5. Interpretability:

<img src="interpretability1.png" width="80%" height="80%">

<img src="interpretability2.png" width="80%" height="80%">