​​GraphML News (April 23rd) - Topological Deep Learning, Scalable Molecular Simulations, Network Games

Architectures of Topological Deep Learning: A Survey on Topological Neural Networks by Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, and Nina Miolane - a wonderful survey on Topological Deep Learning explaining basic concepts from sets and graphs to simplicial and cellular complexes using message passing framework. The survey also covers prominent deep learning architectures employing topological features and tasks that benefit from them. Must read 👍

Scaling the leading accuracy of deep equivariant models to biomolecular simulations of realistic size by Albert Musaelian, Anders Johansson, Simon Batzner, Boris Kozinsky - the work introduces Allegro v2, an improved version of the SOTA equivariant model Allegro, probed on the humongous problem scale: nanoseconds of the full HIV capsid (44M atoms) and scaling up to 100M atom structures on 5120 A100 GPUs 👀.

New blogs:

Michael Bronstein and Emanuele Rossi wrote an article on Learning Network Games - an intersection of the game theory and Graph ML. The main task is to infer the network structure between the agents in a game based on the observations of actions and outcomes.

Not directly about graphs, but Shashank Prasanna wrote an intro to torch.compile() introduced in PyTorch 2.0 and what’s happening under the hood when you execute it on your model.