Before anything else, let’s break down the title. “Certifiable” means that we can prove mathematically that our model is robust to certain types of attacks (like adding noise or flipping edges). And “robust training” refers to a technique for improving the performance of GCNs by making them more resistant to these same kinds of perturbations during training.

Now, Let’s roll with some code! The authors provide an implementation in PyTorch that you can easily run using their Jupyter notebook demo.py (which is included in the repo). Here’s a quick rundown of what it does:

1. Load your dataset and preprocess it as needed (e.g., convert to sparse format, normalize features)
2. Define your GCN model using their provided code template
3. Train your model on your data using the robust training technique described in the paper
4. Evaluate your model’s performance on a test set and compare it to other methods (like vanilla GCN or adversarial training)
5. Visualize your results using matplotlib, which is also included in their setup requirements

That’s pretty much it! The code is well-documented and easy to follow, so you should be able to get up and running quickly even if you’re new to GCNs or PyTorch. And the best part? It’s all open source, which means that you can modify it as needed for your own projects (or contribute back to the community).

So what are some of the key takeaways from this paper? Well, first and foremost, they show that robust training can significantly improve the performance of GCNs on various benchmark datasets. For example:

– On Cora, their model achieves a test accuracy of 84.3% (compared to 82.9% for vanilla GCN)
– On Citeseer, they get an accuracy of 75.6% (vs. 71.0%)
– And on PubMed, it’s 78.9% vs. 74.3%

These results are pretty impressive considering that the authors also add a “robustness penalty” to their loss function during training, which encourages the model to learn more robust features (i.e., ones that are less sensitive to perturbations). And they show that this technique can significantly reduce the number of adversarial examples in your test set as well!

But perhaps even more importantly, the authors also provide a theoretical analysis of their method using tools from convex optimization and graph theory. This allows them to prove mathematically that their model is certifiably robust against certain types of attacks (like adding noise or flipping edges), which is a major breakthrough in the field of GCNs.

So if you’re interested in learning more about this exciting new paper, be sure to check out the code and resources provided by the authors on their GitHub page!