CIM Seminar with Sergio Pequito

Abstract: Dynamical networks permeate a vast array of natural and human-engineered systems. Our focus often revolves around ensuring the directed evolution of their states towards desired objectives within predefined temporal constraints. The network topologies at play inherently introduce intricate trade-offs, wherein the interplay between the minimal count of influenced nodes and the time required for control comes to the fore. Intriguingly, our investigation reveals that various measures of centrality and non-centrality lack the capacity to elucidate these trade-offs comprehensively. Consequently, widely employed generative models demonstrate their inadequacy in encapsulating the nuanced dynamics under scrutiny.
 
Within this seminar, we present an innovative generative model designed to replicate the nuanced trade-offs observed in actual dynamic networks. In essence, we underscore the pivotal role played by the amalgamation of network partitions and degree distributions in shaping the efficacy of our proposed generative framework. We firmly assert that this methodological approach stands as a linchpin when deploying generative models for the purpose of unraveling the dynamic network attributes spanning the realms of science and engineering.
 
Lastly, our empirical findings substantiate the capacity of the proposed generative model to spawn a diverse spectrum of networks, bearing trade-offs statistically akin to those exhibited by genuine networks. These encompass a range of domains, from neural networks to social networks, wherein the minimum count of manipulated nodes is balanced against the time required to achieve control.

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