Post #899

GraphML News (Aug 3rd) - Graph Foundation Models from Google, PyG ecosystem expanding It’s been a while since the last post, let’s catch up with the news! 🔮 ICML brought a handful of announcements, eg, our team at Google published a blog post on the in-house Graph Foundation Model which particularly excels on relational data and brings nice (3-40x) benefits compared to SOTA tabular models. It’s quite astounding that the tabular ML world has been overlooking graph modeling for this kind of data for many years leaving lots of performance on table. Well, as we said last year, GFMs are already here and will continue to improve across all axes, from systems and infra to modeling and better generalization. 🌟 Besides that, ICML published a list of outstanding paper awards and a handful of them do use graph learning in one way or another - this is an excellent reminder that beating old benchmarks by 1% is not that important (looking at you, ZINC aficionados) but smart application of this tool in appropriate cases (and actually designing those cases) is very promising, encouraged by the community, and can bring insights even in the LLM & agentic era. 🔥 The PyG world is expanding - PyG maintainers released an overview paper on PyG 2.0 and its latest features including first-class support for explainability, heterogeneous graphs, and scalability improvements. RelBench and relational data seem to be the main blockbuster use-cases of those features, and it’s great to see PyG is keeping the bar high ⛳ Another fresh addition to the ecosystem is the Torch Geometric Pool library that expands the variety of pooling functions. ⌛Temporal Graph Modeling (TGM) is another new PyG-based library designed for temporal and dynamic graphs. It already bundles several standard baselines like TGN and TGAT, GraphMixer, and EdgeBank, as well as datasets such as Temporal Graph Benchmark. Have a look at the accompanying preprint.