Connectivity-Based Source Selection is a source population selection method that identifies and prioritizes source domains based on the strength and structure of inter-domain connectivity metrics, such as data distribution similarity, feature space overlap, or transferability estimates, to optimize knowledge transfer in cross-domain learning scenarios.