We are finally starting to see the real impact of Geometric machine learning techniques in fundamental science.

Here are the opinions of prominent researchers in the field of graph ML and its applications trying to summarise the...

This post argues that the graph isomorphism setting is too limiting for analysing the expressive power of graph neural networks...

This post discusses how to design local and computationally efficient provably powerful graph neural networks that are not based on...

Do you have a feeling that deep learning on graphs is a bunch of heuristics that work sometimes and nobody...

Graph neural networks exploit relational inductive biases for data that come in the form of a graph. However, in many...

Most of the architectures used in graph deep learning are shallow with just a handful of layers. Does depth in...

One of the challenges that have so far precluded the wide adoption of graph neural networks in industrial applications is...

Have you even wondered what is so special about convolution? Know how to derive the convolution from first principles and...

Deep learning on graphs, also known as Geometric deep learning (GDL) , Graph representation learning (GRL), or relational inductive biases...

Many real-world problems involving networks of transactions of various nature and social interactions and engagements are dynamic and can be...