diff --git a/README.md b/README.md index 742e826..5c0b47b 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@
-Source code for the paper "**[Graph Neural Networks with Learnable Structural and Positional Representations](https://openreview.net/pdf?id=wTTjnvGphYj)**" by Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio and Xavier Bresson, at the **Tenth International Conference on Tenth International Conference on Learning Representations (ICLR) 2022**. +Source code for the paper "**[Graph Neural Networks with Learnable Structural and Positional Representations](https://openreview.net/pdf?id=wTTjnvGphYj)**" by Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio and Xavier Bresson, at the **Tenth International Conference on Learning Representations (ICLR) 2022**. 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.