Score-driven generalized fitness model for sparse and weighted temporal networks

Temporal network data have recently received increasing attention due to the rich information content and valuable insight that appropriate modeling of links’ dynamics can unveil. While most of the literature on temporal network models focuses on binary graphs, each link of a real networks is often associated with a weight, a positive number describing the intensity of the relation between the nodes. Here we propose a novel dynamical model for sparse and weighted temporal networks as a combination of an extension of the fitness model and of the score-driven framework. We consider a zero-augmented generalized linear model to handle the weights and an observation-driven approach to describe time-varying parameters. We propose a flexible approach where the existence probability of a link is independent of its expected weight. This fact represents a crucial difference with alternative specifications proposed in the recent literature, with relevant implications both for the model's flexibility and for the forecasting capability. Our approach also accommodates the network dynamics’ dependence on external variables. We present a link forecasting analysis to data describing the overnight exposures in the Euro interbank market and investigate whether the influence of EONIA rates on the interbank network dynamics has changed over time during the sovereign debt crisis. (c) 2022 Elsevier Inc. All rights reserved

Reconstructing Topological Properties of Complex Networks Using the Fitness Model

A major problem in the study of complex socioeconomic systems is represented by privacy issues—that can put severe limitations on the amount of accessible information, forcing to build models on the basis of incomplete knowledge. In this paper we investigate a novel method to reconstruct global topological properties of a complex network starting from limited information. This method uses the knowledge of an intrinsic property of the nodes (indicated as fitness), and the number of connections of only a limited subset of nodes, in order to generate an ensemble of exponential random graphs that are representative of the real systems and that can be used to estimate its topological properties. Here we focus in particular on reconstructing the most basic properties that are commonly used to describe a network: density of links, assortativity, clustering. We test the method on both benchmark synthetic networks and real economic and financial systems, finding a remarkable robustness with respect to the number of nodes used for calibration. The method thus represents a valuable tool for gaining insights on privacy-protected systems

Reconstructing topological properties of complex networks using the fitness model.

Theoretical Physic