İsmail İlkan Ceylan

A single graph foundation model learns to wander across diverse graph structures and tasks, achieving SOTA on node classification, homogeneous link prediction, and KG link prediction.

Looped flows are proposed, an approach that allows solving harder problems by spending more computation through a finer temporal grid and enables multiple valid predictions from different initial noise samples.

Boost macrocycle generation rates from 1% to 99% by guiding diffusion models with persistent homology, opening new avenues for drug discovery.