Friday Graph ML News: ProGen, ClimaX, WebConf Workshops, Everything is Connected

No week without new foundation models!

A collaboration of researchers from Salesforce, UCSF and Berkeley announced ProGen, an LLM for protein sequence generation. Claimed to be “ChatGPT for proteins”, ProGen is a 1.2B model trained on 280M sequences controllable by input tags, eg “Protein Family: Pfam ID PF16754, Pesticin”. The authors synthesized in a lab a handful of generated proteins to confirm model quality.

In the GraphML’23 State of Affairs we highlighted weather prediction models GraphCast (from DeepMind) and PanguWeather (from Huawei). This week, Microsoft Research and UCLA announced ClimaX, the foundation model for climate and weather that can serve as a backbone for many many downstream applications. In contrast to now-casting GraphCast and PanguWeather, ClimaX is tailored for more long-range predictions up to a month. ClimaX is a ViT-based image-to-image model with several tokenization and representation novelties to account for different input granularity and sequence length - check out the full paper preprint for more details.

Petar Veličković published the opinion paper Everything is Connected: Graph Neural Network framing many ML applications through the lens of graph representation learning. The article gives a gentle introduction to the basics of GNNs and their applications including geometric equivariant models. Nice read!

The WebConf’23 (April 30 - May 4) announced accepted workshops with a handful of Graph ML venues:

- Graph Neural Networks: Foundation, Frontiers and Applications
- Mining of Real-world Hypergraphs: Patterns, Tools, and Generators
- Graph Neural Networks for Tabular Data Learning
- Continual Graph Learning
- Towards Out-of-Distribution Generalization on Graphs
- Self-supervised Learning and Pre-training on Graphs
- When Sparse Meets Dense: Learning Advanced Graph Neural Networks with DGL-Sparse Package