GraphML News (Oct 28th) - Stanford Graph Workshop, NeurIPS workshops

The Stanford Graph Learning workshop happened on Tuesday, 24th and I was privileged to visit it in person and deliver a talk about our recent ULTRA - it was great to meet old friends and many new folks from the graph learning area! The recording will (hopefully) be available soon. Among big announcements was TorchFrame from the authors of PyG - a library for deep learning on tabular data (before Kaggle grandmasters enter the berserker mode 👺 - yes it allows to run xgboost and catboost) that has uniform interfaces for training and inferencing over huge dataframes. PyG but for tabular data!

MoML on Nov 8th at MIT is the next big venue for the geometric learning and drug discovery communities.

NeurIPS workshops decisions are (mostly) out - check out your inboxes if you submitted anything. Some already have the proceedings available on OpenReview: Generative AI and Bio, Deep Generative Models for Health, AI4Science. Have a look there - usually such workshops contain precursors and hints to what will be published by big labs soon.

A new blogpost Topological Generalisation with Advective Diffusion Transformers by Michael Bronstein, Qitian Wu, and Chenxiao Wang introducing ADiT, Advective Diffusion Transformers.

Weekend reading:

Graph Deep Learning for Time Series Forecasting by Andrea Cini et al (IDSIA)

Talk like a Graph: Encoding Graphs for Large Language Models by Bahare Fatemi et al (Google)

Towards Foundational Models for Molecular Learning on Large-Scale Multi-Task Datasets by Dominique Beaini et al (Valence, Mila)

Graph Positional and Structural Encoder by Renming Liu, Semih Cantürk et al (Michigan, Mila)