arXiv subjects

Cristian E. La Rocca

Publications and source records attributed to Cristian E. La Rocca.

13 recordsLinked to original sources

arXiv2025

Recovery of contour nodes in interdependent directed networks

Extensive research has focused on studying the robustness of interdependent non-directed networks and the design of mitigation strategies aimed at reducing disruptions caused by cascading failures. However, real systems such as power and communication networks are directed, which underscores the necessity of broadening the analysis by including directed networks. In this work, we develop an analytical framework to study a recovery strategy in two interdependent directed networks in which a fraction $q$ of nodes in each network have single dependencies with nodes in the other network. Following the random failure of nodes that leaves a fraction $p$ intact, we repair a fraction of nodes that are neighbors of the giant strongly connected component of each network with probability or recovery success rate $γ$. Our analysis reveals an abrupt transition between total system collapse and complete recovery as $p$ is increased. As a consequence, we identify three distinct phases in the $(p, γ)$ parameter space: collapse despite intervention, recovery enabled by the strategy, and resilience without intervention. Moreover, we demonstrate our strategy on a system built from empirical data and find that it can save resources compared to a random recovery strategy. Our findings underscore the potential of targeted recovery strategies to enhance the robustness of real interdependent directed networks against cascading failures.

physics.soc-ph↗

arXiv2024

Critical behavior of cascading failures in overloaded networks

While network abrupt breakdowns due to overloads and cascading failures have been studied extensively, the critical exponents and the universality class of such phase transitions have not been discussed. Here, we study breakdowns triggered by failures of links and overloads in networks with a spatial characteristic link length $ζ$. Our results indicate that this abrupt transition has features and critical exponents similar to those of interdependent networks, suggesting that both systems are in the same universality class. For weakly embedded systems (i.e., $ζ$ of the order of the system size $L$) we observe a mixed-order transition, where the order parameter collapses following a long critical plateau. On the other hand, strongly embedded systems (i.e., $ζ\ll L$) exhibit a pure first-order transition, involving nucleation and the growth of damage. The system's critical behavior in both limits is similar to that observed in interdependent networks.

physics.soc-ph↗

arXiv2021

An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system and the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the $4^\circ$ most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring these predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy, in which a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of $τ$ days. Besides, we implement a random testing strategy over the population. We found that $τ= 7$ is a good choice for the quarantine strategy since it matches with the weekly cycle as it reduces the infected population. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30\% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, being the equipment required to deal with the additional critical patients relatively low.

q-bio.PE↗

arXiv2020

Cascading Failures in Complex Networks

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems, and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behavior. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.

physics.soc-ph↗

arXiv2020

Controlling distant contacts to reduce disease spreading on disordered complex networks

In real social networks, person-to-person interactions are known to be heterogeneous, which can affect the way a disease spreads through a population, reaches a tipping point in the fraction of infected individuals, and becomes an epidemic. This property, called disorder, is usually associated with contact times between individuals and can be modeled by a weighted network, where the weights are related to normalized contact times $ω$. In this paper, we study the SIR model for disease spreading when both close and distant types of interactions are present. We develop a mitigation strategy that reduces only the time duration of distant contacts, which are easier to alter in practice. Using branching theory, supported by simulations, we found that the effectiveness of the strategy increases when the density $f_1$ of close contacts decreases. Moreover, we found a threshold $\tilde{f}_1 = T_c / β$ below which the strategy can bring the system from an epidemic to a non-epidemic phase, even when close contacts have the longest time durations.

physics.soc-ph↗

arXiv2018

Strategy for stopping failure cascades in interdependent networks

Interdependencies are ubiquitous throughout the world. Every real-world system interacts with and is dependent on other systems, and this interdependency affects their performance. In particular, interdependencies among networks make them vulnerable to failure cascades, the effects of which are often catastrophic. Failure propagation fragments network components, disconnects them, and may cause complete systemic failure. We propose a strategy of avoiding or at least mitigating the complete destruction of a system of interdependent networks experiencing a failure cascade. Starting with a fraction $1-p$ of failing nodes in one network, we reconnect with a probability $γ$ every isolated component to a functional giant component (GC), the largest connected cluster. We find that as $γ$ increases the resilience of the system to cascading failure also increases. We also find that our strategy is more effective when it is applied in a network of low average degree. We solve the problem theoretically using percolation theory, and we find that the solution agrees with simulation results.

physics.soc-ph↗

arXiv2017

Epidemic spreading in multiplex networks influenced by opinion exchanges on vaccination

We study the changes of opinions about vaccination together with the evolution of a disease. In our model we consider a multiplex network consisting of two layers. One of the layers corresponds to a social network where people share their opinions and influence others opinions. The social model that rules the dynamic is the M-model, which takes into account two different processes that occurs in a society: persuasion and compromise. This two processes are related through a parameter $r$, $r<1$ describes a moderate and committed society, for $r>1$ the society tends to have extremist opinions, while $r=1$ represents a neutral society. This social network may be of real or virtual contacts. On the other hand, the second layer corresponds to a network of physical contacts where the disease spreading is described by the SIR-Model. In this model the individuals may be in one of the following four states: Susceptible ($S$), Infected($I$), Recovered ($R$) or Vaccinated ($V$). A Susceptible individual can: i) get vaccinated, if his opinion in the other layer is totally in favor of the vaccine, ii) get infected, with probability $β$ if he is in contact with an infected neighbor. Those $I$ individuals recover after a certain period $t_r=6$. Vaccinated individuals have an extremist positive opinion that does not change. We consider that the vaccine has a certain effectiveness $ω$ and as a consequence vaccinated nodes can be infected with probability $β(1 - ω)$ if they are in contact with an infected neighbor. In this case, if the infection process is successful, the new infected individual changes his opinion from extremist positive to totally against the vaccine. We find that depending on the trend in the opinion of the society, which depends on $r$, different behaviors in the spread of the epidemic occurs. An epidemic threshold was found.

physics.soc-ph↗

arXiv2016

Fluctuations of a surface relaxation model in interacting scale free networks

Isolated complex networks have been studied deeply in the last decades due to the fact that many real systems can be modeled using these types of structures. However, it is well known that the behavior of a system not only depends on itself, but usually also depends on the dynamics of other structures. For this reason, interacting complex networks and the processes developed on them have been the focus of study of many researches in the last years. One of the most studied subjects in this type of structures is the Synchronization problem, which is important in a wide variety of processes in real systems. In this manuscript we study the synchronization of two interacting scale-free networks, in which each node has $ke$ dependency links with different nodes in the other network. We map the synchronization problem with an interface growth, by studying the fluctuations in the steady state of a scalar field defined in both networks. We find that as $ke$ slightly increases from $ke=0$, there is a really significant decreasing in the fluctuations of the system. However, this considerable improvement takes place mainly for small values of $ke$, when the interaction between networks becomes stronger there is only a slight change in the fluctuations. We characterize how the dispersion of the scalar field depends on the internal degree, and we show that a combination between the decreasing of this dispersion and the integer nature of our growth model are the responsible for the behavior of the fluctuations with $ke$.

physics.soc-ph↗

arXiv2015

Synchronization in Scale Free networks: The role of finite size effects

Synchronization problems in complex networks are very often studied by researchers due to its many applications to various fields such as neurobiology, e-commerce and completion of tasks. In particular, Scale Free networks with degree distribution $P(k)\sim k^{-λ}$, are widely used in research since they are ubiquitous in nature and other real systems. In this paper we focus on the surface relaxation growth model in Scale Free networks with $2.5< λ<3$, and study the scaling behavior of the fluctuations, in the steady state, with the system size $N$. We find a novel behavior of the fluctuations characterized by a crossover between two regimes at a value of $N=N^*$ that depends on $λ$: a logarithmic regime, found in previous research, and a constant regime. We propose a function that describes this crossover, which is in very good agreement with the simulations. We also find that, for a system size above $N^{*}$, the fluctuations decrease with $λ$, which means that the synchronization of the system improves as $λ$ increases. We explain this crossover analyzing the role of the network's heterogeneity produced by the system size $N$ and the exponent of the degree distribution.

physics.soc-ph↗

arXiv2012

Competition between surface relaxation and ballistic deposition models in scale free networks

In this paper we study the scaling behavior of the fluctuations in the steady state $W_S$ with the system size $N$ for a surface growth process given by the competition between the surface relaxation (SRM) and the Ballistic Deposition (BD) models on degree uncorrelated Scale Free networks (SF), characterized by a degree distribution $P(k)\sim k^{-λ}$, where $k$ is the degree of a node. It is known that the fluctuations of the SRM model above the critical dimension ($d_c=2$) scales logarithmically with $N$ on euclidean lattices. However, Pastore y Piontti {\it et. al.} [A. L. Pastore y Piontti {\it et. al.}, Phys. Rev. E {\bf 76}, 046117 (2007)] found that the fluctuations of the SRM model in SF networks scale logarithmically with $N$ for $λ<3$ and as a constant for $λ\geq 3$. In this letter we found that for a pure ballistic deposition model on SF networks $W_S$ scales as a power law with an exponent that depends on $λ$. On the other hand when both processes are in competition, we find that there is a continuous crossover between a SRM behavior and a power law behavior due to the BD model that depends on the occurrence probability of each process and the system size. Interestingly, we find that a relaxation process contaminated by any small contribution of ballistic deposition will behave, for increasing system sizes, as a pure ballistic one. Our findings could be relevant when surface relaxation mechanisms are used to synchronize processes that evolve on top of complex networks.

physics.soc-ph↗

arXiv2012

A mechanism to synchronize fluctuations in scale free networks using growth models

In this paper we study the steady state of the fluctuations of the surface for a model of surface growth with relaxation to any of its lower nearest neighbors (SRAM) [F. Family, J. Phys. A {\bf 19}, L441 (1986)] in scale free networks. It is known that for Euclidean lattices this model belongs to the same universality class as the model of surface relaxation to the minimum (SRM). For the SRM model, it was found that for scale free networks with broadness $λ$, the steady state of the fluctuations scales with the system size $N$ as a constant for $λ\geq 3$ and has a logarithmic divergence for $λ< 3$ [Pastore y Piontti {\it et al.}, Phys. Rev. E {\bf 76}, 046117 (2007)]. It was also shown [La Rocca {\it et al.}, Phys. Rev. E {\bf 77}, 046120 (2008)] that this logarithmic divergence is due to non-linear terms that arises from the topology of the network. In this paper we show that the fluctuations for the SRAM model scale as in the SRM model. We also derive analytically the evolution equation for this model for any kind of complex graphs and find that, as in the SRM model, non-linear terms appear due to the heterogeneity and the lack of symmetry of the network. In spite of that, the two models have the same scaling, but the SRM model is more efficient to synchronize systems.

cond-mat.stat-mech↗

arXiv2012

Synchronization in Scale Free networks with degree correlation

In this paper we study a model of synchronization process on scale free networks with degree-degree correlations. This model was already studied on this kind of networks without correlations by Pastore y Piontti {\it et al.}, Phys. Rev. E {\bf 76}, 046117 (2007). Here, we study the effects of the degree-degree correlation on the behavior of the load fluctuations $W_s$ in the steady state. We found that for assortative networks there exist a specific correlation where the system is optimal synchronized. In addition, we found that close to this optimally value the fluctuations does not depend on the system size and therefore the system becomes fully scalable. This result could be very important for some technological applications. On the other hand, far from the optimal correlation, $W_s$ scales logarithmically with the system size.

physics.soc-ph↗

arXiv2012

Conservative model for synchronization problems in complex networks

In this paper we study the scaling behavior of the interface fluctuations (roughness) for a discrete model with conservative noise on complex networks. Conservative noise is a noise which has no external flux of deposition on the surface and the whole process is due to the diffusion. It was found that in Euclidean lattices the roughness of the steady state $W_s$ does not depend on the system size. Here, we find that for Scale-Free networks of $N$ nodes, characterized by a degree distribution $P(k)\sim k^{-λ}$, $W_s$ is independent of $N$ for any $λ$. This behavior is very different than the one found by Pastore y Piontti {\it et. al} [Phys. Rev. E {\bf 76}, 046117 (2007)] for a discrete model with non-conservative noise, that implies an external flux, where $W_s \sim \ln N$ for $λ< 3$, and was explained by non-linear terms in the analytical evolution equation for the interface [La Rocca {\it et. al}, Phys. Rev. E {\bf 77}, 046120 (2008)]. In this work we show that in this processes with conservative noise the non-linear terms are not relevant to describe the scaling behavior of $W_s$.

cond-mat.stat-mech↗