Inferring Network Structures via Signal Lasso

TL;DR

This paper introduces signal Lasso, a new method for inferring unweighted, sparse network structures from data, demonstrating improved accuracy over classical techniques in various network applications.

Contribution

The paper presents the signal Lasso approach, a novel technique specifically designed for reconstructing binary, sparse network topologies, with detailed theoretical analysis and practical validation.

Findings

Outperforms classical methods in accuracy and mean square error

Reliable and robust in synthetic and empirical network applications

Effective in inferring unweighted, sparse network structures

Abstract

Inferring the connectivity structure of networked systems from data is an extremely important task in many areas of science. Most of real-world networks exhibit sparsely connected topologies, with links between nodes that in some cases may be even associated to a binary state (0 or 1, denoting respectively the absence or the existence of a connection). Such un-weighted topologies are elusive to classical reconstruction methods such as Lasso or Compressed Sensing techniques. We here introduce a novel approach called signal Lasso, where the estimation of the signal parameter is subjected to 0 or 1 values. The theoretical properties and algorithm of proposed method are studied in detail. Applications of the method are illustrated to an evolutionary game and synchronization dynamics in several synthetic and empirical networks, where we show that the novel strategy is reliable and robust, and outperform the classical approaches in terms of accuracy and mean square errors.