Post #811

GraphML News (Dec 9th) - NeurIPS’23, MatterGen, new blogs, PygHO 🎷 NeurIPS’23 starts on Sunday in jazzy New Orleans including tons of Graph ML papers and workshops that we covered in the previous articles (search by “NeurIPS workshop”). Find Michael (jetlagged from Dagstuhl) in the unique meme-designed t-shirt at two poster sessions (one, two) to chat about papers, graphs, or relay your POV on the diffusion vs flow matching feud of the year. ⚛️ Following the announcements of UniMat and GNoME from DeepMind, MSR AI 4 Science announced MatterGen, a new generative model for inorganic materials design. Practically, unconditional MatterGen is a diffusion model based on the GemNet backbone with both continuous and discrete diffusion components, ie, continuous diffusion is applied to lattice parameters and fractional coordinates, discrete diffusion (absorbing state with the MASK token) is applied to atom compositions. A pre-trained MatterGen can then be steered in many directions with classifier-free guidance, and the authors report conditioning on target chemistry, energy, magnetic properties, and on a practical use-case of designing magnets. Seems like big labs are picking up on materials science and it will be a key topic of generative models in 2024 along with molecules and proteins. Meanwhile, a few new blog posts have arrived: - Cooperative GNNs by Ben Finkelshtein, Ismail Ceylan, Xingyue Huang, and Michael Bronstein on the recently proposed GNN architecture; - Equivariant CNNs and steerable kernels - part 3 of the series based off the monumental book Equivariant CNN by Maurice Weiler Xiyuan Wang and Muhan Zhang published PyTorch Geometric Higher Order (PygHO), a library that implements a collection of primitives to create higher-order GNNs (like subgraph GNNs, PPGN, Nested GNNs) and data wrappers with proper graph transformations. Weekend reading: MatterGen: a generative model for inorganic materials design by Zeni et al. - the MatterGen paper Expressive Sign Equivariant Networks for Spectral Geometric Learning (NeurIPS’23) by Derek Lim, Joshua Robinson, Stefanie Jegelka, and Haggai Maron - extension of invariant SignNet to sign equivariance Recurrent Distance-Encoding Neural Networks for Graph Representation Learning by Yuhui Ding et al. - Linear Recurrent Units (LRUs) straight from NLP arrived to GNNs Variational Annealing on Graphs for Combinatorial Optimization by Sebastian Sanokowski feat. Sepp Hochreiter