Inferring network structures via signal Lasso - Lei Shi; Chen Shen; Libin Jin; Qi Shi; Zhen Wang; Stefano Boccaletti (Physical Review Research, 2021)
Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most real-world networks exhibit sparsely connected topologies, with links between nodes that in some cases may even be associated with a binary state (0 or 1, denoting, respectively, the absence or presence of a connection). Such un-weighted topologies are elusive to classical reconstruction methods such as Lasso or compressed sensing techniques. We here introduce an approach called signal Lasso, in which the estimation of the signal parameter is subjected to 0 or 1 values. The theoretical properties and algorithm of the proposed method are studied in detail. Applications of the method are illustrated for an evolutionary game and synchronization dynamics in several synthetic and empirical networks, for which we show that our strategy is reliable and robust and outperforms the classical approaches in terms of accuracy and mean square errors.