PALMS: Parallel Adaptive Lasso with Multi-directional Signals for Latent Networks Reconstruction

Large-scale networks exist in many field and play an important role in real-world dynamics. However, the networks are usually latent and expensive to detect, which becomes the main challenging for many applications and empirical analysis. Several statistical methods were proposed to infer the edges, but the complexity of algorithms make them hard to be applied for large-scale networks. In this paper, we proposed a general distributed and parallel computing framework for network reconstruction methods via compressive sensing technical, to make them feasible for inferring the super large networks in practice. Combining with the CALMS, we proposed for those estimators enjoy additional theoretical properties, such as the consistency and asymptotic normality, we prove that the approximate estimation utilizing the distributed algorithm can keep the theoretical results.