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Complex networks theory is used to represents real-world systems than can be viewed as ensembles of interacting units (the nodes) coupled through a graph (the network topology). Both in natural and artificial settings, the nodes and the network topology coevolve, and their evolution cannot be studied independently. Departing from this consideration, this line of research (started in collaboration with Prof. Maurizio Porfiri, head of the Dynamical System group at the Dynamical Systems Laboratory of the NYU Tandon School of Engineering) considered the network topology itself a dynamical systems, whose evolution is bidirectionally coupled with the dynamics at the nodes.
Leveraging this paradigm, my research then focused on the analysis of financial markets, as they are characterized by intricate patterns of influence among the agents. The final goal is to elucidate the delicate interplay between the topological evolution of the graph describing the mutual influence among the agent and the individual agent behavior.
Selected publications:
F. Della Rossa, L. Giannini, P. De Lellis, “Herding or wisdom of the crowd? Controlling efficiency in a partially rational financial market”, PLoS One, 15(9), e0239132, 2020.
P. DeLellis, A. Di Meglio, F. Lo Iudice, “Overconfident agents and evolving financial networks”, Nonlinear Dynamics, 92(1), 33-40, 2018.
P. DeLellis, A. DiMeglio, F. Garofalo, F. Lo Iudice, “The evolving cobweb of relations among partially rational investors”, PLoS One, 12(2), e0171891, 2017.
P. DeLellis, F. Garofalo, F. Lo Iudice, E. Napoletano, "Wealth distribution across communities of adaptive financial agents", New Journal of Physics, 17(8), 083003, 2015.
P. DeLellis, M. diBernardo, F. Garofalo, M. Porfiri, “Evolution of Complex Networks via Edge Snapping”, IEEE Transactions on Circuits and Systems I, 57(8), 2132-2143, 2010
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