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 Attention Networks Under the Hood
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