  We introduce a kernel Lasso (kLasso) optimization that simultaneously
accounts for spatial regularity and network sparsity to reconstruct spatial
complex networks from data. Through a kernel function, the proposed approach
exploits spatial embedding distances to penalize overabundance of spatially
long-distance connections. Examples of both synthetic and real-world spatial
networks show that the proposed method improves significantly upon existing
network reconstruction techniques that mainly concerns sparsity but not spatial
regularity. Our results highlight the promise of data fusion in the
reconstruction of complex networks, by utilizing both microscopic node-level
dynamics (e.g., time series data) and macroscopic network-level information
(metadata).
