links

README.md

@@ -4,9 +4,9 @@
 
 <br>
 
-Source code for the paper "**Graph Neural Networks with Learnable Structural and Positional Representations**" by Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio and Xavier Bresson.
+Source code for the paper "**[Graph Neural Networks with Learnable Structural and Positional Representations](https://arxiv.org/abs/2110.07875)**" by Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio and Xavier Bresson.
 
-We propose a novel GNN architecture in which the structural and positional representations are decoupled, and are learnt separately to learn these two essential properties. The architecture, named **MPGNNs-LSPE** (MPGNNs with **L**earnable **S**tructural and **P**ositional **E**ncodings), is generic enough that it can be applied to any GNN model of interest which fits into the popular 'message-passing framework', including Transformers.
+We propose a novel GNN architecture in which the structural and positional representations are decoupled, and are learnt separately to learn these two essential properties. The architecture, named **MPGNNs-LSPE** (MPGNNs with **L**earnable **S**tructural and **P**ositional **E**ncodings), is generic that it can be applied to any GNN model of interest which fits into the popular 'message-passing framework', including Transformers.
 
 ![MPGNNs-LSPE](./docs/gnn-lspe.png)  
 
@@ -36,13 +36,12 @@ We propose a novel GNN architecture in which the structural and positional repre
 
 ## 4. Reference 
 
-TODO
-[ArXiv's paper](https://arxiv.org/pdf/2110.xxxxx.pdf)
+:page_with_curl: Paper [on arXiv](https://arxiv.org/abs/2110.07875)
 ```
 @article{dwivedi2021graph,
   title={Graph Neural Networks with Learnable Structural and Positional Representations},
   author={Dwivedi, Vijay Prakash and Luu, Anh Tuan and Laurent, Thomas and Bengio, Yoshua and Bresson, Xavier},
-  journal={arXiv preprint arXiv:2110.xxxxx},
+  journal={arXiv preprint arXiv:2110.07875},
   year={2021}
 }
 ```