Topic: graph-structured data

Floris Geerts analyses the expressive power of graph-learning methods. Predictions on graph-structured data must respect isomorphism, motivating invariant operations such as neighbourhood aggregation. The lecture connects these compositional operations with database theory to explain what different graph-learning architectures can represent, with applications including molecules, social networks and recommendation systems.

Schizophrenia is considered to be a complex illness with multiple pathways contributing to its clinical phenotype and is argued to be a disorder of disrupted brain network organization. Structural covariance in schizophrenia indicates a system-level brain maturation disruption overlapping with patterns of disturbed functional connectivity. Structural covariance networks are typically computed at the group-level, which prevents individualized inferences. To advance precision medicine of schizophrenia, a reliable mapping of brain system-level heterogeneity at the subject-level is critical. Here,