GraphML News (May 10th) - PageRank and New Pope, Scientific Agents, more blogs

🤌 🇻🇦 Researchers from Bocconi University in Milan rolled the best usage of network science of 2025: using centrality measures to predict the results of the conclave (who elects the next Pope). They mined a graph of Vatican cardinals according to their job duties, informal relationships, and “spiritual genealogies”, and computed a bunch of centrality measures - eigenvector centrality (probably a PageRank), betweenness centrality (affordable for small networks), and some clustering metrics. One of them did rank the real elected Pope in the top (although others didn’t have him in top-5) which is a cool result. Good ole PageRank still makes headlines in 2025!

🦅 FutureHouse announced the Platform for scientific discovery tasks. Practically, the Platform combines 4 distinct multimodal agents (avian beings): Crow for search, Falcon for deep search, Owl for questions a-la “has anyone done X before”, and Phoenix as the next-gen ChemCrow for molecular design. Agents accept whatever text and image inputs you have at hand, and will search a huge collection of scientific documents. Having worked with PaperQA before, I have a good experience with FH tools - there might be more announcements coming soon about new scientific results achieved with those agents

✍️ More blogposts! Kumo is on the writing spree: a massive post on relational graph transformers for RelBench that improves over GNNs (but I bet is much faster and more scalable) and a more technical writeup on enabling torch.compile for GNNs which results in 30% training speedups. ProTip: GNNs in JAX are already JIT’table from the very beginning 😉

🧬 AITHYRA announced the AI4Science symposium to take place in Vienna on September 8-10 with top speakers from AI and Life Sciences areas.

Weekend reading:

System of Agentic AI for the Discovery of Metal-Organic Frameworks by Theo Jaffrelot Inizan, Sherry Yang, Aaron Kaplan, Yen-hsu Lin, and a team of UC Berkeley and DeepMind researchers - another take on the multi-agent discovery pipeline combining LLMs, diffusion models, and ML potentials for creating new metal-organic frameworks (MOFs) that helped synthesizing 5 new structures.

Plexus: Taming Billion-edge Graphs with 3D Parallel GNN Training by Aditya Ranjan and U of Maryland - a new platform to scale GNNs to supercomputers, tried 2048 GPUs on Frontier and Perlmutter on graphs up to OGB Papers100M.