Graph Neural Networks

I didn’t make it to Punta Cana this year 😢 but I’m happy (remotely) for the folks who managed to get there in spite of all traveling restrictions! Premium content inside. The autumn got very busy and I’d like to try a shorter format: each big topic has one “spotlight” 🛋 work in the main block which I find particularly interesting, and several relevant works which have a bit … [Read more...] about Knowledge Graphs At EMNLP 2021

This is part four in a series on graph theory and graph convolutional networks. If you’ve been reading this whole series, you’ve been with me on this entire journey — through discussing what graph theory is and why it matters, what a graph convolutional network even is and how they work, and now we’re here, to the fun part — building our own GCN. If … [Read more...] about Graph Convolutional Network Implementation With the PROTEINS Benchmark Dataset

This is the third part of a series on graphs and graph theory in machine learning. By now, if you’ve been following this series, you may have learned a bit about graph theory, why we care about graph structured data in data science, and what the heck a “Graph Convolutional Network” is. Now, I’d like to briefly introduce you to what makes these things work. For my … [Read more...] about What Makes Graph Convolutional Networks Work?

In my last article on graph theory, I briefly introduced my latest topic of interest: Graph Convolutional Networks. If you’re here thinking “what do those words mean?”, you’re in the right place. In this article, we’re going to break this topic down, step by step. Part I: What’s This Graph Thing? If this is the first you’re hearing this ‘graph’ word, I’m sorry, but you … [Read more...] about Graph Convolutional Networks — Explained

This blog is a part of my “deep learning update” series and I want to open it up with a question: What have I learned about Graph ML in 2+ months? Nothing? If that was your first thought, no worries, it’s probably true. 😅 (Bad) jokes aside, my “relative knowledge” (the knowledge I “possess” vs the knowledge I’m aware of) is asymptotically … [Read more...] about How To Get Started With Graph Machine Learning

This article describes how to extend the simplest formulation of Graph Neural Networks (GNNs) to encode the structure of multi-relational data, such as Knowledge Graphs (KGs). The article includes 4 main sections: an introduction to the key idea of multi-relational data, which describes the peculiarity of KGs;a summary of the standard components included in a GNN … [Read more...] about Graph Neural Networks for Multi-Relational Data

Graph Neural Networks (GNNs) have emerged as the standard toolbox to learn from graph data. GNNs are able to drive improvements for high-impact problems in different fields, such as content recommendation or drug discovery. Unlike other types of data such as images, learning from graph data requires specific methods. As defined by Michael Bronstein: [..] these methods … [Read more...] about Graph Attention Networks Under the Hood