Update README.md

dig/xgraph/TAGE/README.md

@@ -1,16 +1,17 @@
 # Task-Agnostic Graph Explanations
 
-This is the official implementation of the paper [*"Task-Agnostic Graph Explanations"*](https://arxiv.org/abs/2202.08335) appear in NeurIPS 2022.
-
-## Abstract
-
-Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. Existing learning-based GNN explanation approaches are task-specific in training and hence suffer from crucial drawbacks. Specifically, they are incapable of producing explanations for a multitask prediction model with a single explainer. They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks. To address these limitations, we propose a Task-Agnostic GNN Explainer (TAGE) that is independent of downstream models and trained under self-supervision with no knowledge of downstream tasks. TAGE enables the explanation of GNN embedding models with unseen downstream tasks and allows efficient explanation of multitask models. Our extensive experiments show that TAGE can significantly speed up the explanation efficiency by using the same model to explain predictions for multiple downstream tasks while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches. 
+This is the official implementation of the paper [*"Task-Agnostic Graph Explanations"*](https://arxiv.org/abs/2202.08335) appears in NeurIPS 2022.
 
 <p align="center">
-<img src="https://github.com/divelab/DIG/blob/main/dig/xgraph/TAGE/pipeline.jpg" width="700" class="center" alt="task-agnostic"/>
+    <br/>
+    <img src="https://github.com/divelab/DIG/blob/main/dig/xgraph/TAGE/pipeline.jpg" width="900" class="center" alt="task-agnostic"/>
     <br/>
 </p>
 
+## Abstract
+
+Graph Neural Networks (GNNs) have emerged as powerful tools to encode graph-structured data. Due to their broad applications, there is an increasing need to develop tools to explain how GNNs make decisions given graph-structured data. Existing learning-based GNN explanation approaches are task-specific in training and hence suffer from crucial drawbacks. Specifically, they are incapable of producing explanations for a multitask prediction model with a single explainer. They are also unable to provide explanations in cases where the GNN is trained in a self-supervised manner, and the resulting representations are used in future downstream tasks. To address these limitations, we propose a Task-Agnostic GNN Explainer (TAGE) that is independent of downstream models and trained under self-supervision with no knowledge of downstream tasks. TAGE enables the explanation of GNN embedding models with unseen downstream tasks and allows efficient explanation of multitask models. Our extensive experiments show that TAGE can significantly speed up the explanation efficiency by using the same model to explain predictions for multiple downstream tasks while achieving explanation quality as good as or even better than current state-of-the-art GNN explanation approaches. 
+
 ## Environment Requirements
 - jupyter
 - pytorch
@@ -30,10 +31,11 @@ We have provided trained GNN models to be explained. The trained explainers are
 ## Bibtex
 
 If you use this code, please cite the paper.
-
+```
 @inproceedings{xie2022task,
   title={Task-Agnostic Graph Explanations},
   author={Xie, Yaochen and Katariya, Sumeet and Tang, Xianfeng and Huang, Edward and Rao, Nikhil and Subbian, Karthik and Ji, Shuiwang},
   booktitle={The 36th Annual Conference on Neural Information Processing Systems},
   year={2022}
 }
+```