arXiv subjects

Francesco Sorrentino

Publications and source records attributed to Francesco Sorrentino.

At least 19 recordsLinked to original sources

arXiv2026

Extreme vulnerability to intruder attacks destabilizes network dynamics

Consensus, synchronization, formation control, and power grid balance are examples of desirable dynamical states that arise in networks. Here we investigate how such states can be destabilized by an intruder agent within an otherwise functioning network. We see that a single adversarial node, coupled through adversarial connections to one or more other nodes, is sufficient to destabilize the entire network. We further show that concentrating the attack on a single low-indegree node induces the greatest instability, challenging the common assumption that hubs are the most critical nodes. This leads to a new characterization of network vulnerability, identifying low-indegree nodes as the most vulnerable components. Although derived for linear systems, our results extend to nonlinear networks, including the Kuramoto model. These findings reveal an intrinsic vulnerability of technological, social, and biological networks.

nlin.AO↗

arXiv2026

Optimizing the Network Topology of a Linear Reservoir Computer

Machine learning has become a fundamental approach for modeling, prediction, and control, enabling systems to learn from data and perform complex tasks. Reservoir computing is a machine learning tool that leverages high-dimensional dynamical systems to efficiently process temporal data for prediction and observation tasks. Traditionally, the connectivity of the network that underlies a reservoir computer (RC) is generated randomly, lacking a principled design. Here, we focus on optimizing the connectivity of a linear RC to improve its performance and interpretability, which we achieve by decoupling the RC dynamics into a number of independent modes. We then proceed to optimize each one of these modes to perform a given task, which corresponds to selecting an optimal RC connectivity in terms of a given set of eigenvalues of the RC adjacency matrix. Simulations on networks of varying sizes show that the optimized RC significantly outperforms randomly constructed reservoirs in both training and testing phases and often surpasses nonlinear reservoirs of comparable size. This approach provides both practical performance advantages and theoretical guidelines for designing efficient, task-specific, and analytically transparent RC architectures.

eess.SY↗

arXiv2026

The Frequency Response of Networks as Open Systems

Many biological, technological, and social systems can be effectively described as networks of interacting subsystems. Typically, these networks are not isolated objects, but interact with their environment through both signals and information that is received by specific nodes with an input function or released to the environment by other nodes with an output function. An important question is whether the structure of different networks, together with the particular selection of input and output nodes, is such that it favors the passing or blocking of such signals. For a given network and a given choice of the input and output nodes, the H2-norm provides a natural and general quantification of the extent to which input signals, whether deterministic or stochastic, periodic or arbitrary, are amplified. We analyze a diverse set of empirical networks and find that many naturally occurring systems, such as food webs, signaling pathways, and gene regulatory circuits, are structurally organized to enhance the passing of signals; in contrast, the structure of engineered systems like power grids appears to be intentionally designed to suppress signal propagation.

eess.SY↗

arXiv2025

Open networks in discrete time: Passing vs blocking behavior

This paper presents a unified framework for analyzing the input-output behavior of discrete time complex networks viewed as open systems. Importantly, we focus on systems that are inherently modeled in discrete time-such as opinion dynamics, Markov chains, diffusion on networks, and population models-reflecting their natural formulation in many real-world contexts. By an open network, we mean one that is coupled to its environment, through both external signals that are received by designated input nodes and response signals that are released back into the environment via a separate set of output nodes. We develop a general framework for characterizing whether such networks amplify (pass) or suppress (block) the external inputs. Our approach combines the transfer function of the network with the discrete time controllability Gramian, using the H2-norm to quantify signal amplification. We introduce a computationally efficient network index based on the Gramian trace and eigenvalues, enabling scalable comparisons across network topologies. Application of our method to a broad set of empirical networks, spanning biological, technological, and ecological domains, uncovers consistent structural signatures associated with passing or blocking behavior. These findings shed light on how the network architecture and the particular selection of input and output nodes shape information flow in real-world systems, with broad implications for control, signal processing, and network design.

physics.soc-ph↗

arXiv2025

Constructing directed networks with a desired minimum balanced coloring

This work introduces a systematic algorithm for generating directed networks with prescribed symmetries by constructing expansions from a given quotient network. The method enables researchers to synthesize realistic network models with controllable symmetry structure, facilitating studies of symmetry-driven dynamics such as cluster synchronization in biological, social, and technological systems.

physics.soc-ph↗

arXiv2025

RCUKF: Data-Driven Modeling Meets Bayesian Estimation

Accurate modeling is crucial in many engineering and scientific applications, yet obtaining a reliable process model for complex systems is often challenging. To address this challenge, we propose a novel framework, reservoir computing with unscented Kalman filtering (RCUKF), which integrates data-driven modeling via reservoir computing (RC) with Bayesian estimation through the unscented Kalman filter (UKF). The RC component learns the nonlinear system dynamics directly from data, serving as a surrogate process model in the UKF prediction step to generate state estimates in high-dimensional or chaotic regimes where nominal mathematical models may fail. Meanwhile, the UKF measurement update integrates real-time sensor data to correct potential drift in the data-driven model. We demonstrate RCUKF effectiveness on well-known benchmark problems and a real-time vehicle trajectory estimation task in a high-fidelity simulation environment.

cs.LG↗

arXiv2025

Bridging the Gap between Reactivity, Contraction, and Finite-Time Lyapunov Exponents

Reactivity, contractivity, and Lyapunov exponents are powerful tools for studying the stability properties of dynamical systems and have been extensively investigated in the literature for decades. In this paper, we review and extend the concepts of reactivity, contractivity, and finite-time Lyapunov exponents for discrete-time dynamical systems and establish connections among them. We focus on time-invariant maps, time-varying linear maps, and certain classes of time-varying nonlinear maps. In particular, we show that if the corresponding $p$-iteration systems (with p > 1) are contractive, then the original systems admit stable attractors such as fixed points or limit cycles. We demonstrate the application of these results to the analysis of synchronization stability in coupled networks and discuss how p-iteration systems can serve as a useful framework for studying network synchronization.

math.DS↗

arXiv2025

Symmetries of Living Systems: Symmetry Fibrations and Synchronization in Biological Networks

A symmetry is a `change without change'. As simple as it sounds, this concept is the fundamental cornerstone that unifies all branches of theoretical physics. Virtually all physical laws -- ranging from classical mechanics and electrodynamics to relativity, quantum mechanics, and the standard model -- can be expressed in terms of symmetry invariances. In this book, we explore whether the same principle can also explain the emergent laws of biological systems. We introduce a new geometry for biological networks and AI architectures, drawing inspiration from the mystic genius of Grothendieck's fibrations in category theory. We attempt to bridge the gap between physics and biology using symmetries but with a twist. The traditional symmetry groups of physics are global and too rigid to describe biology. Instead, the novel notion of symmetry fibration is local, flexible, and adaptable to evolutionary pressures, providing the right framework for understanding biological complexity. In other words, this more general symmetry invariance is necessary and sufficient to ensure that a given biological network configuration can support a synchronized function. In this book, we review the theoretical progress over the last decades from mathematics, physics, computer science, dynamical systems, and graph theory that has led to the discovery of symmetry fibrations in biological networks. These symmetries act as organizing principles for biological networks. They serve as effective tools for describing the structure of these networks, blending geometry and topology. Fibrations explain how structure dictates function across various biological domains, including the transcriptome, proteome, metabolome, and connectome. Additionally, they facilitate a reduction in the dimensionality of the network, simplifying it into its fundamental building blocks for biological computation.

q-bio.MN↗

arXiv2024

The Efficiency of Synchronization Dynamics and the Role of Network Syncreactivity

Synchronization of coupled oscillators is a fundamental process in both natural and artificial networks. While much work has investigated the asymptotic stability of the synchronous solution, the fundamental question of the transient behavior toward synchronization has received far less attention. In this work, we present the transverse reactivity as a metric to quantify the instantaneous rate of growth or decay of desynchronizing perturbations. We first use the transverse reactivity to design a coupling-efficient and energy-efficient synchronization strategy that involves varying the coupling strength dynamically according to the current state of the system. We find that our synchronization strategy is able to synchronize networks in both simulation and experiment over a significantly larger (often by orders of magnitude) range of coupling strengths than is possible when the coupling strength is constant. Then, we characterize the effects of network topology on the transient dynamics towards synchronization by introducing the concept of network syncreactivity: A network with a larger syncreactivity has a larger transverse reactivity at every point on the synchronization manifold, independent of the oscillator dynamics. We classify real-world examples of complex networks in terms of their syncreactivity.

nlin.AO↗

arXiv2023

Synchronization in networked systems with large parameter heterogeneity

Systems that synchronize in nature are intrinsically different from one another, with possibly large differences from system to system. While a vast part of the literature has investigated the emergence of network synchronization for the case of small parametric mismatches, we consider the general case that parameter mismatches may be large. We present a unified stability analysis that predicts why the range of stability of the synchronous solution either increases or decreases with parameter heterogeneity for a given network. We introduce a parametric approach, based on the definition of a curvature contribution function, which allows us to estimate the effect of mismatches on the stability of the synchronous solution in terms of contributions of pairs of eigenvalues of the Laplacian. For cases in which synchronization occurs in a bounded interval of a parameter, we study the effects of parameter heterogeneity on both transitions (asynchronous to synchronous and synchronous to asynchronous.)

eess.SY↗

arXiv2023

Single-Integrator Consensus Dynamics over Minimally Reactive Networks

The problem of achieving consensus in a network of connected systems arises in many science and engineering applications. In contrast to previous works, we focus on the system reactivity, i.e., the initial amplification of the norm of the system states. We identify a class of networks that we call minimally reactive, which are such that the indegree and the outdegree of each node of the network are the same. We propose several optimization procedures in which minimum perturbations (links or link weights) are imposed on a given network topology to make it minimally reactive. A new concept of structural reactivity is introduced which measures how much a given network is far from becoming minimally reactive by link perturbations. The structural reactivity of directed random graphs is studied.

eess.SY↗

arXiv2023

Synchronizing Chaos using Reservoir Computing

We attempt to achieve isochronal synchronization between a drive system unidirectionally coupled to a response system, under the assumption that limited knowledge on the states of the drive is available at the response. Machine learning techniques have been previously implemented to estimate the states of a dynamical system from limited measurements. We consider situations in which knowledge of the non-measurable states of the drive system is needed in order for the response system to synchronize with the drive. We use a reservoir computer to estimate the non-measurable states of the drive system from its measured states and then employ these measured states to synchronize the response system with the drive.

nlin.AO↗

arXiv2023

Time-shift selection for reservoir computing using a rank-revealing QR algorithm

Reservoir computing, a recurrent neural network paradigm in which only the output layer is trained, has demonstrated remarkable performance on tasks such as prediction and control of nonlinear systems. Recently, it was demonstrated that adding time-shifts to the signals generated by a reservoir can provide large improvements in performance accuracy. In this work, we present a technique to choose the time-shifts by maximizing the rank of the reservoir matrix using a rank-revealing QR algorithm. This technique, which is not task dependent, does not require a model of the system, and therefore is directly applicable to analog hardware reservoir computers. We demonstrate our time-shift selection technique on two types of reservoir computer: one based on an opto-electronic oscillator and the traditional recurrent network with a $tanh$ activation function. We find that our technique provides improved accuracy over random time-shift selection in essentially all cases.

cs.LG↗

arXiv2023

Reservoir Computing with Noise

This paper investigates in detail the effects of noise on the performance of reservoir computing. We focus on an application in which reservoir computers are used to learn the relationship between different state variables of a chaotic system. We recognize that noise can affect differently the training and testing phases. We find that the best performance of the reservoir is achieved when the strength of the noise that affects the input signal in the training phase equals the strength of the noise that affects the input signal in the testing phase. For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.

cs.NE↗

arXiv2022

Pinning control of networks: dimensionality reduction through simultaneous block-diagonalization of matrices

In this paper, we study the network pinning control problem in the presence of two different types of coupling: (i) node-to-node coupling among the network nodes and (ii) input-to-node coupling from the source node to the `pinned nodes'. Previous work has mainly focused on the case that (i) and (ii) are of the same type. We decouple the stability analysis of the target synchronous solution into subproblems of the lowest dimension by using the techniques of simultaneous block diagonalization (SBD) of matrices. Interestingly, we obtain two different types of blocks, driven and undriven. The overall dimension of the driven blocks is equal to the dimension of an appropriately defined controllable subspace, while all the remaining undriven blocks are scalar. Our main result is a decomposition of the stability problem into four independent sets of equations, which we call quotient controllable, quotient uncontrollable, redundant controllable, and redundant uncontrollable. Our analysis shows that the number and location of the pinned nodes affect the number and the dimension of each set of equations. We also observe that in a large variety of complex networks, stability of the target synchronous solution is de facto only determined by a single quotient controllable block.

eess.SY↗

arXiv2022

Symmetry-driven network reconstruction through pseudobalanced coloring optimization

Symmetries found through automorphisms or graph fibrations provide important insights in network analysis. Symmetries identify clusters of robust synchronization in the network which improves the understanding of the functionality of complex biological systems. Network symmetries can be determined by finding a {\it balanced coloring} of the graph, which is a node partition in which each cluster of nodes receives the same information (color) from the rest of the graph. In recent work we saw that biological networks such as gene regulatory networks, metabolic networks and neural networks in organisms ranging from bacteria to yeast and humans are rich in fibration symmetries related to the graph balanced coloring. Networks based on real systems, however, are built on experimental data which are inherently incomplete, due to missing links, collection errors, and natural variations within specimens of the same biological species. Therefore, it is fair to assume that some of the existing symmetries were not detected in our analysis. For that reason, a method to find pseudosymmetries and repair networks based on those symmetries is important when analyzing real world networks. In this paper we introduce the {\it pseudobalanced coloring} \eqref{eq:mainip} problem, and provide an integer programming formulation which (a) calculates a pseudobalanced coloring of the graph taking into account the missing data, and (b) optimally repairs the graph with the minimal number of added/removed edges to maximize the symmetry of the graph. We apply our formulation to the {\it C. elegans} connectome to find pseudocoloring and the optimal graph repair. Our solution compares well with a manually curated ground-truth {\it C. elegans} graph as well as solutions generated by other methods of missing link prediction.

q-bio.QM↗

arXiv2022

Reply to comment on "Failure of the simultaneous block diagonalization technique applied to complete and cluster synchronization of random networks"

We respond briefly to a comment [1, arXiv:2110.15493] recently posted online on our paper [2, arXiv:2108.07893]. Complete and cluster synchronization of random networks is undoubtedly a topic of interest in the Physics, Engineering, and Nonlinear Dynamics literature. In [3] we study both complete and cluster synchronization of networks and introduce indices that measure success (or failure) of application of the SBD technique in decoupling the stability problem into problems of lower dimensionality. Our usage of the word `failure' indicates that the technique does not produce a decomposition which results in a system which is easier to analyze, not that the technique fails in correctly decoupling these problems.

eess.SY↗

arXiv2022

Comment on "Synchronization dynamics in non-normal networks: the trade-off for optimality"

We review some of the recent literature, including Refs. [1-3], on the effects of non-normality on the synchronization of networks of oscillators, and provide numerical evidence that the basin of attraction about the synchronous solution is typically smaller for networks with non-normal Laplacian matrix, compared to networks with a normal Laplacian matrix.

nlin.AO↗