arXiv subjects

Ivan Bonamassa

Publications and source records attributed to Ivan Bonamassa.

16 recordsLinked to original sources

arXiv2026

Localization transitions of diffusion dynamics in physical networks

Network diffusion underlies many transport phenomena, with Laplacian modes setting how information spreads and relaxes. In physical networks, however, connectivity alone is not enough: node volumes introduce local dwell times that regulate how flow is stored before being propagated. Here we show that physical heterogeneity reshapes topology-driven localization, with the degree-volume ratio emerging as the relevant disorder parameter. We solve an analytical model in which ratio detuning qualitatively reorganizes the Laplacian spectrum, and demonstrate in empirical networks how degree-volume correlations shift extremal eigenmodes away from the nodes selected by topology alone. Our results reveal a general feature-rich-driven mechanism for localization control, showing that physicality non-trivially reshapes the disorder landscape governing network dynamics.

cond-mat.stat-mech↗

arXiv2024

Network dismantling by physical damage

We explore the robustness of complex networks against physical damage. We focus on spatially embedded network models and datasets where links are physical objects or physically transfer some quantity, which can be disrupted at any point along its trajectory. To simulate physical damage, we tile the networks with boxes of equal size and sequentially damage them. By introducing an intersection graph to keep track of the links passing through tiles, we systematically analyze the connectivity of the network and explore how the physical layout and the topology of the network jointly affect its percolation threshold. We show that random layouts make networks extremely vulnerable to physical damage, driven by the presence of very elongated links, and that higher-dimensional embeddings further increase their vulnerability. We compare this picture against targeted physical damages, showing that it accelerates network dismantling and yields non-trivial geometric patterns. Finally, we apply our framework to several empirical networks, from airline networks to vascular systems and the brain, showing qualitative agreement with the theoretical predictions.

cond-mat.stat-mech↗

arXiv2024

The microscopic origin of abrupt transitions in interdependent systems

Phase transitions are fundamental features of statistical physics. While the well-studied continuous phase transitions are known to be controlled by external \textit{macroscopic} changes in the order parameter, the origin of abrupt transitions is not yet clear. Here we show that abrupt phase transitions may occur due to a unique internal \textit{microscopic} cascading mechanism, resulting from dependency interactions. We experimentally unveil the underlying mechanism of the abrupt transition in interdependent superconducting networks to be governed by a unique metastable state of a long-living resistance cascading plateau. This plateau is characterized by spontaneous \textit{microscopic} changes that last for \textit{thousands} of seconds, followed by a \textit{macroscopic} phase shift of the system. Similar microscopic mechanisms are expected to be found in a variety of systems showing abrupt transitions.

physics.soc-ph↗

arXiv2023

Dynamics of cascades in spatial interdependent networks

The dynamics of cascading failures in spatial interdependent networks significantly depend on the interaction range of dependency couplings between layers. In particular, for increasing range of dependency couplings, different types of phase transition accompanied by various cascade kinetics can be observed including mixed-order transition characterized by critical branching phenomena, first-order transition with nucleation cascades, and continuous second-order transition with weak cascades. We also describe the dynamics of cascades at the mutual mixed-order resistive transition in interdependent superconductors and show its similarity to that of percolation of interdependent abstract networks. Finally, we layout our perspectives for the experimental observation of these phenomena, their phase diagrams and the underlying kinetics, in the context of physical interdependent networks. Our studies of interdependent networks shed light on the possible mechanisms of three known types of phase transitions, second order, first order, and mixed order as well as predicting a novel fourth type where a microscopic intervention will yield a macroscopic phase transition.

physics.soc-ph↗

arXiv2023

Microscopic intervention yields abrupt transition in interdependent magnetic networks

The study of interdependent networks has recently experienced a boost with the development of experimentally testable materials that physically realize their critical behaviors, calling for systematic studies that go beyond the percolation paradigm. Here we study the critical phase transition of interdependent spatial magnetic networks model where dependency couplings between networks are realized by a thermal interaction having a tunable spatial range. We show how the critical phenomena and the phase diagram of this realistic model are highly affected by the range of thermal dissipation and how the latter changes the transition from continuous to abrupt. Furthermore, we show that microscopic interventions of localized heating and localized magnetic field yield a macroscopic phase transition and novel phase diagrams. Our results provide novel and realistic insights about controlling the macroscopic phases of interdependent materials by means of localized microscopic interventions.

physics.soc-ph↗

arXiv2023

Physical networks as network-of-networks

Physical networks are made of nodes and links that are physical objects embedded in a geometric space. Understanding how the mutual volume exclusion between these elements affects the structure and function of physical networks calls for a suitable generalization of network theory. Here, we introduce a network-of-networks framework where we describe the shape of each extended physical node as a network embedded in space and these networks are bound together by physical links. Relying on this representation, we introduce a minimal model of network growth and we show for a general class of physical networks that volume exclusion induces heterogeneity in both node volume and degree, with the two becoming correlated. These emergent properties strongly affect the dynamics on physical networks: by calculating their Laplacian spectrum as a function of the coupling strength between the nodes we show that degree-volume correlations suppress the role of hubs as early spreaders in diffusive dynamics. We apply the network-of-networks framework to describe several real systems and find properties analog to the minimal model networks. The prevalence of these properties points towards general growth mechanisms that do not depend on the specifics of the systems.

cond-mat.stat-mech↗

arXiv2022

Fractal fluctuations at mixed-order transitions in interdependent networks

We study the geometrical features of the order parameter's fluctuations near the critical point of mixed-order phase transitions in randomly interdependent spatial networks. In contrast to continuous transitions, where the structure of the order parameter at criticality is fractal, in mixed-order transitions the structure of the order parameter is known to be compact. Remarkably, we find that although being compact, the fluctuations of the order parameter close to mixed-order transitions are fractal up to a well-defined correlation length $ξ'$, which diverges when approaching the critical threshold. We characterize the self-similar nature of these critical fluctuations through their fractal dimension, $d_f'=3d/4$, and correlation length exponent, $ν'=2/d$, where $d$ is the dimension of the system. By means of percolation and magnetization, we demonstrate that $d_f'$ and $ν'$ are independent on the symmetry of the underlying process for any $d$ of the underlying networks.

cond-mat.dis-nn↗

arXiv2021

Realizing interdependent couplings as thermal or higher-order interactions

Interdependence is a fundamental ingredient to analyze the stability of many real-world complex systems featuring functional liasons. Yet, physical realizations of this coupling are still unknown, due to the lack of a theoretical framework for their study. To address this gap, we develop an interdependent magnetization framework and show that dependency links between $K-1$ pairwise networks of Ising spins can be rigorously mapped to directed $K$-spin interactions or to adaptive thermal couplings. We adopt the thermal portrait to determine analytically the phase diagram of the model under different structural configurations and we corroborate our results by extensive simulations. We find that interdependence acts like an entropic force that amplifies site-to-site thermal fluctuations, yielding unusual forms of vulnerability and making the system's functioning often unrecoverable. Finally, we discover an isomorphism between the ground state of random multi-spin models and interdependent percolation on randomly coupled networks. This connection raises new perspectives of cross-fertilization, providing unfamiliar methods with relevant implications in the study of constraint satisfaction as well as to the functional robustness of interdependent systems.

cond-mat.dis-nn↗

arXiv2020

Network Geometry

Real networks are finite metric spaces. Yet the geometry induced by shortest path distances in a network is definitely not its only geometry. Other forms of network geometry are the geometry of latent spaces underlying many networks, and the effective geometry induced by dynamical processes in networks. These three approaches to network geometry are all intimately related, and all three of them have been found to be exceptionally efficient in discovering fractality, scale-invariance, self-similarity, and other forms of fundamental symmetries in networks. Network geometry is also of great utility in a variety of practical applications, ranging from the understanding how the brain works, to routing in the Internet. Here, we review the most important theoretical and practical developments dealing with these approaches to network geometry in the last two decades, and offer perspectives on future research directions and challenges in this novel frontier in the study of complexity.

physics.soc-ph↗

arXiv2020

Interdependent transport via percolation backbones in spatial networks

The functionality of nodes in a network is often described by the structural feature of belonging to the giant component. However, when dealing with problems like transport, a more appropriate functionality criterion is for a node to belong to the network's backbone, where the flow of information and of other physical quantities (such as current) occurs. Here we study percolation in a model of interdependent resistor networks and show the effect of spatiality on their coupled functioning. We do this on a realistic model of spatial networks, featuring a Poisson distribution of link-lengths. We find that interdependent resistor networks are significantly more vulnerable than their percolation-based counterparts, featuring first-order phase transitions at link-lengths where the mutual giant component still emerges continuously. We explain this apparent contradiction by tracing the origin of the increased vulnerability of interdependent transport to the crucial role played by the dandling ends. Moreover, we interpret these differences by considering an heterogeneous $k$-core percolation process which enables to define a one-parameter family of functionality criteria whose constraints become more and more stringent. Our results highlight the importance that different definitions of nodes functionality have on the collective properties of coupled processes, and provide better understanding of the problem of interdependent transport in many real-world networks.

physics.soc-ph↗

arXiv2020

Geometric characterization of SARS-CoV-2 pandemic events

While the SARS-CoV-2 keeps spreading world-wide, comparing its evolution across different nations is a timely challenge of both theoretical and practical importance. The large variety of dissimilar and country-dependent epidemiological factors, in fact, makes extremely difficult to understand their influence on the epidemic trends within a unique and coherent framework. We present a geometric framework to characterize, in an integrated and low-dimensional fashion, the epidemic plume-like trajectories traced by the infection rate, $I$, and the fatality rate, $D$, in the $(I,D)$ plane. Our analysis enables the definition of an epidemiometric system based on three geometric observables rating the SARS-CoV-2 pandemic events via scales analogous to those for the magnitude and the intensity of seismic events. Being exquisitely geometric, our framework can be applied to classify other epidemic data and secondary waves, raising the possibility of designing epidemic alerts or early warning systems to enhance public and governmental responses to a rapidly emerging outbreak.

physics.soc-ph↗

arXiv2019

Critical stretching of mean-field regimes in spatial networks

We study a spatial network model with exponentially distributed link-lengths on an underlying grid of points, undergoing a structural crossover from a random, Erdős--Rényi graph to a $2D$ lattice at the characteristic interaction range $ζ$. We find that, whilst far from the percolation threshold the random part of the incipient cluster scales linearly with $ζ$, close to criticality it extends in space until the universal length scale $ζ^{3/2}$ before crossing over to the spatial one. We demonstrate this {\em critical stretching} phenomenon in percolation and in dynamical processes, and we discuss its implications to real-world phenomena, such as neural activation, traffic flows or epidemic spreading.

physics.soc-ph↗

arXiv2018

Spontaneous repulsion in the $A+B\to0$ reaction on coupled networks

We study the transient dynamics of an $A+B \rightarrow 0$ process on a pair of randomly coupled networks, where reactants are initially separated. We find that, for sufficiently small fractions $q$ of cross-couplings, the concentration of $A$ (or $B$) particles decays linearly in a first stage and crosses over to a second linear decrease at a mixing time $t_x$. By numerical and analytical arguments, we show that for symmetric and homogeneous structures $t_x\propto(\nicefrac{\langle k \rangle}{q})\log(\nicefrac{\langle k \rangle}{q})$ where $\langle k \rangle$ is the mean degree of both networks. Being this behavior in marked contrast with a purely diffusive process---where the mixing time would go simply like $\langle k\rangle/q$---we identify the logarithmic slowing down in $t_x$ to be the result of a novel spontaneous mechanism of {\em repulsion} between the reactants $A$ and $B$ due to the interactions taking place at the networks' interface. We show numerically how this spontaneous repulsion effect depends on the topology of the underlying networks.

cond-mat.stat-mech↗

arXiv2017

Critical field-exponents for secure message-passing in modular networks

We study secure message-passing in the presence of multiple adversaries in modular networks. We assume a dominant fraction of nodes in each module have the same vulnerability, i.e., the same entity spying on them. We find both analytically and via simulations that the links between the modules (interlinks) have effects analogous to a magnetic field in a spin system in that for any amount of interlinks the system no longer undergoes a phase transition. We then define the exponents $δ$, which relates the order parameter (the size of the giant secure component) at the critical point to the field strength (average number of interlinks per node), and $γ$, which describes the susceptibility near criticality. These are found to be $δ=2$ and $γ=1$ (with the scaling of the order parameter near the critical point given by $β=1$). When two or more vulnerabilities are equally present in a module we find $δ=1$ and $γ=0$ (with $β\geq2$). Apart from defining a previously unidentified universality class, these exponents show that increasing connections between modules is more beneficial for security than increasing connections within modules. We also measure the correlation critical exponent $ν$, and the upper critical dimension $d_c$, finding that $νd_c=3$ as for ordinary percolation, suggesting that for secure message-passing $d_c =6$. These results provide an interesting analogy between secure message-passing in modular networks and the physics of magnetic spin-systems.

physics.soc-ph↗

arXiv2017

Dynamic interdependence and competition in multilayer networks

From critical infrastructure, to physiology and the human brain, complex systems rarely occur in isolation. Instead, the functioning of nodes in one system often promotes or suppresses the functioning of nodes in another. Despite advances in structural interdependence, modeling interdependence and other interactions between dynamic systems has proven elusive. Here we define a broadly applicable dynamic dependency link and develop a general framework for interdependent and competitive interactions between general dynamic systems. We apply our framework to studying interdependent and competitive synchronization in multi-layer oscillator networks and cooperative/competitive contagions in an epidemic model. Using a mean-field theory which we verify numerically, we find explosive transitions and rich behavior which is absent in percolation models including hysteresis, multi-stability and chaos. The framework presented here provides a powerful new way to model and understand many of the interacting complex systems which surround us.

cond-mat.stat-mech↗

arXiv2016

Synchronization in networks with multiple interaction layers

The structure of many real-world systems is best captured by networks consisting of several interaction layers. Understanding how a multi-layered structure of connections affects the synchronization properties of dynamical systems evolving on top of it is a highly relevant endeavour in mathematics and physics, and has potential applications to several societally relevant topics, such as power grids engineering and neural dynamics. We propose a general framework to assess stability of the synchronized state in networks with multiple interaction layers, deriving a necessary condition that generalizes the Master Stability Function approach. We validate our method applying it to a network of Rössler oscillators with a double layer of interactions, and show that highly rich phenomenology emerges. This includes cases where the stability of synchronization can be induced even if both layers would have individually induced unstable synchrony, an effect genuinely due to the true multi-layer structure of the interactions amongst the units in the network.

physics.soc-ph↗