Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks

By Michael Bronstein — 8 min read

In this post, I discuss how to design local and computationally efficient provably powerful graph neural networks that are not based on the Weisfeiler-Lehman tests hierarchy. This is the second in the series of posts on the expressivity of graph neural networks. See Part 1 describing the relation between graph neural networks and the Weisfeiler-Lehman graph isomorphism test. In Part 3, I will argue why we should abandon the graph isomorphism problem altogether.

Programming Fairness in Algorithms

By Matthew Stewart, PhD Researcher — 22 min read

Supervised machine learning algorithms are inherently discriminatory. They are discriminatory in the sense that they use information embedded in the features of data to separate instances into distinct categories — indeed, this is their designated purpose in life.

Interactive differential expression analysis with volcano3D

By Katriona Goldmann — 5 min read

I am pleased to present volcano3D, an R package which is now available on CRAN! The volcano3D package enables exploration of probes differentially expressed between three groups. Its main purpose is for the visualisation of differentially expressed genes in a three-dimensional volcano plot.