Abstract We introduce a kernel Lasso (kLasso) approach which is type of sparse optimization that simultaneously accounts for spatial regularity and structural sparsity to reconstruct spatially embedded complex networks from time-series data about nodal states. Through the design function motivated by real-world network features, proposed kLasso exploits embedding distances penalize overabundanc...