IDENTIFICATION OF TIME-VARYING SYSTEMS AND NETWORK RECONSTRUCTION

We proposed a method to identify the size of nonlinear network systems, including dynamics on hypergraphs, from perceptible dynamics:

• Brovia F.B., Zino L., Succar R., Porfiri M., Inference of the size of nonlinear network systems from perceptible dynamics, to appear in Chaos, 2026.

 

We developed a method to identify piecewise constant parameters for nonlinear systems:

• Calafiore G.C., Fracastoro G., Zino L., Identification of Piecewise Constant Parameters in Nonlinear Models. European Journal of Control, 80, 101151, 2024.

 

We developed a formalism to represent and reconstruct time-varying networks with communities and strong ties. Our efforts are summarized in the following list of publications:

• Bongiorno C., Zino L., and Rizzo A., On unveiling the community structure of temporal networks. Proceedings of the 57th IEEE Conference on Decision and Control, Dec 2018, Miami Beach FL, US

• Bongiorno C., Zino L., and Rizzo A., A novel framework for community modeling and characterization in directed temporal networks. Applied Network Science, 4(10), 2019

• Surano F.V., Bongiorno C., Zino L., Porfiri M., and Rizzo A., Backbone Reconstruction in Temporal Networks from Epidemic Data. Physical Review E, 100, 042306, 2019