Update README.md

Author: thupchnsky

README.md

@@ -1,31 +1,55 @@
-### Dependencies
+# Unlearning Graph Classifiers with Limited Data Resources
+A powerful and efficient method for graph unlearning when the size of training dataset is limited ([TheWebConf 2023 paper](https://arxiv.org/pdf/2211.03216.pdf)).
 
-torch, torchvision, scikit-learn, pytorch-dp, pytorch-geometric, tqdm
-
-### Setup
-
-We assume the following project directory structure:
+# Package Information
+```
+pytorch
+scikit-learn
+pytorch-dp
+pytorch-geometric
+tqdm
+```
 
+# Usage
+### Project Setup
+We assume the following project directory structure in our code:
 ```
 <root>/
 --> data/
 --> results/
 --> scripts/
 ```
+If you have a different path for the datasets, you might need to change the utility function in `datasets.py`.
 
-### Usage:
+### Example
 
--To reproduce Table 3:
+- To reproduce the results in Table 1 and 2 (test accuracy and running time of different backbone graph learning methods without unlearning):
 ```
 ./scripts/Exp1.sh
 ```
+You can change the models or datasets that you want to test with by modifying `Exp1.sh`.
 
--To reproduce Figure 3:
+- To reproduce Figure 3 (test accuracy and running time of different unlearning methods, 10% sequential unlearning requests):
 ```
 ./scripts/Exp2.sh
 ```
 
--To reproduce Figure 4:
+- To reproduce Figure 4 (test accuracy and running time of different unlearning methods, 90% sequential unlearning requests):
 ```
 ./scripts/Exp3.sh
 ```
+
+# Contact
+Please contact Chao Pan (chaopan2@illinois.edu), Eli Chien (ichien3@illinois.edu) if you have any question.
+
+# Citation
+If you find our code or work useful, please consider citing our paper:
+```
+@inproceedings{
+pan2023unlearning,
+title={Unlearning Graph Classifiers with Limited Data Resources},
+author={Chao Pan and Eli Chien and Olgica Milenkovic},
+booktitle={The Web Conference},
+year={2023}
+}
+```