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"Towards Quantifying Long-Range Interactions in Graph Maine Learning: a Large Graph Dataset and a Measurement" by Huidong Liang, Haitz Sáez de Ocáriz Borde, Baskaran Sripathmanathan, Michael Bronstein, xiaowen dong Paper: arxiv.org/abs/2503.09008 #graphneuralnetworks #machinelearning

'Graph Low-Rank Adapters of High Regularity for Graph Neural Networks and Graph Transformers' w/ Pantelis Papageorgiou, Anastasis Kratsios and Michael Bronstein was recently accepted at the ICLR SCOPE workshop Code: github.com/PanPapag/GConv…

A while back, I wrote this explanation about Graph Neural Networks, Transformers, and Graph Transformers, which may be useful for anyone looking for an introduction to the topic. researchgate.net/publication/37… #GNN #Transformers #GraphTransformers

Are you at ICLR in Singapore this week 🇸🇬? Pls feel free to connect & get in touch! We will also present our recent work on learning symmetries as a spotlight in ML for genomics workshop (MLGenX), Sun 27/4! Looking forward to it!

The Graph Attention Network (GAT) update equation in matrix form! (instead of the node-wise eq. in the original paper) Big thanks to Petar Veličković :) Find the complete text in this elucidation: researchgate.net/publication/37…

I believe there are not many theoretical papers on Mixture of Experts? In this work we provide some approximation theory results :) openreview.net/pdf?id=oeg2ncu…

Our long-range measures from 'Towards Quantifying Long-Range Interactions in Graph Machine Learning: a Large Graph Dataset and a Measurement' are now readily usable in PyTorch Geometric: pytorch-geometric.readthedocs.io/en/latest/modu…