Chiara Razzetta

Multimodal brain-connectome analysis usually treats structural connectivity as a propagation channel and functional connectivity as the signal carried through it. A Wasserstein Weisfeiler-Lehman kernel instead represents each subject as one weighted graph with continuous node attributes and compares subjects by optimal transport between point clouds. On a 900-subject synthetic cohort it reaches 0.651±0.028 balanced accuracy, significantly above a raw-feature SVM and a histogram WL kernel, yet replacing the propagation operator with the identity leaves performance essentially unchanged, so the discriminative gain comes mainly from the Wasserstein representation of functional attributes.

Multimodal brain-connectome analysis usually treats structural connectivity as a propagation channel and functional connectivity as the signal carried through it. A Wasserstein Weisfeiler-Lehman kernel instead represents each subject as one weighted graph with continuous node attributes and compares subjects by optimal transport between point clouds. On a 900-subject synthetic cohort it reaches 0.651±0.028 balanced accuracy, significantly above a raw-feature SVM and a histogram WL kernel, yet replacing the propagation operator with the identity leaves performance essentially unchanged, so the discriminative gain comes mainly from the Wasserstein representation of functional attributes.