Speaker
Description
Partitioning a graph into communities is an NP-hard optimization problem with a natural statistical-physics formulation: the optimal partition is the ground state of an antiferromagnetic Potts model, with modularity playing the role of negative energy. Clustering on graph-structured data is a recurring task in fundamental physics, where detector and event data are naturally represented as graphs, as in charged particle tracking and particle-flow reconstruction at colliders and source clustering in astroparticle physics. We frame community detection as energy minimization and solve it with a transformer-based message passing network, where the message and update functions are transformer encoder layers acting on node features. Training follows a denoising-diffusion setting: Gaussian noise is injected into the node community assignments and the network learns to recover the clean configuration, driving the system from local minima towards the minimum-energy state. The loss function couples a physics-derived continuous modularity with a supervised cross-entropy term. We train and validate on LFR benchmarks across different mixing parameters 𝞵, and investigate generalization to real-world networks as an out-of-distribution transfer problem.