add colab

Graph-COM · Jun 12, 2022 · a0bb3ff · a0bb3ff
1 parent eeb06d1
commit a0bb3ff
Show file tree

Hide file tree

Showing 2 changed files with 23 additions and 2 deletions.
diff --git a/README.md b/README.md
@@ -1,4 +1,18 @@
-# Graph Stochastic Attention (GSAT)
+# <center> Graph Stochastic Attention (GSAT) </center>
+
+<p align="center">
+    <a href="https://arxiv.org/abs/2201.12987"><img src="https://img.shields.io/badge/-Paper-grey?logo=gitbook&logoColor=white" alt="Paper"></a>
+    <a href="https://github.com/Graph-COM/GSAT"><img src="https://img.shields.io/badge/-Github-grey?logo=github" alt="Github"></a>
+    <a> <img src="https://img.shields.io/badge/Video-grey?logo=airplayvideo&logoColor=white" alt="Github"></a>
+    <a> <img src="https://img.shields.io/badge/Slides-grey?&logo=MicrosoftPowerPoint&logoColor=white" alt="Github"></a>
+    <a href="https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab"></a>
+    <a href="https://github.com/Graph-COM/GSAT/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/Graph-Com/GSAT?color=blue"> </a>
+</p>
+<!-- <p align="center">
+    <a href="https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab"></a>
+    <a href="https://github.com/Graph-COM/GSAT/blob/main/LICENSE"> <img alt="License" src="https://img.shields.io/github/license/Graph-Com/GSAT?color=blue"> </a>
+</p> -->
+
 The official implementation of GSAT for our paper: [Interpretable and Generalizable Graph Learning via Stochastic Attention Mechanism](https://arxiv.org/abs/2201.12987), to appear in ICML 2022.
 
 ## Introduction
@@ -32,7 +46,7 @@ pip install -r requirements.txt
 
 
 ## Run Examples
-We provide examples with minimal code to run GSAT in `./example/example.ipynb`. We have tested the provided examples on `Ba-2Motifs (GIN)`, `Mutag (GIN)`  and `OGBG-Molhiv (PNA)`. Yet, to implement GSAT* one needs to load a pre-trained model first in the provided example.
+We provide examples with minimal code to run GSAT in `./example/example.ipynb`. We have tested the provided examples on `Ba-2Motifs (GIN)`, `Mutag (GIN)`  and `OGBG-Molhiv (PNA)`. Yet, to implement GSAT* one needs to load a pre-trained model first in the provided example. Also try     <a href="https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colab"></a> to play with `example.ipynb` in Colab.
 
 It should be able to run on other datasets as well, but some hard-coded hyperparameters might need to be changed accordingly. To reproduce results for other datasets, please follow the instructions in the following section.
 

diff --git a/example/example.ipynb b/example/example.ipynb
@@ -1,5 +1,12 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1t0_4BxEJ0XncyYvn_VyEQhxwNMvtSUNx?usp=sharing)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 1,