arXiv subjects

Bnaya Gross

Publications and source records attributed to Bnaya Gross.

At least 19 recordsLinked to original sources

arXiv2026

Two phase transitions in modular multiplex networks

Modular networks, such as critical infrastructures, are often built from distinct, densely connected modules (e.g., cities) that are sparsely interconnected. When such networks are gradually and randomly disrupted under a percolation process, they undergo two critical phase transitions. The first transition occurs when modules become isolated from one another, while the second corresponds to the collapse of the entire network, including the internal connectivity of the modules. Here, we study these phase transitions in modular multiplex networks and compare them with those observed in single-layer modular networks. We focus on models in which the modules are arranged and connected either as a Random Regular network or as a two-dimensional square lattice. We show here that these systems exhibit diverse transition behaviors, with some transitions occurring continuously and others abruptly; notably, one realistic model could display two distinct first-order transitions in the same system. For the modular Random Regular multiplex, we further characterize the spatial transition through its scaling behavior, revealing signatures of a mixed-order phase transitions. In addition, we analytically determine the critical threshold at which modules become disconnected. Our results highlight the crucial role of modular organization and the critical role of interdependence in shaping network vulnerabilities under failures.

physics.soc-ph↗

arXiv2026

The mechanistic origin of branching-driven nucleation in abrupt phase transitions

Phase transitions are the macroscopic manifestation of microscopic processes that drive a system towards a new state. The detailed evolution of these processes, particularly in abrupt phase transitions, are currently not fully understood. Here, we introduce a theoretical framework based on internal node dependencies within a single-layer lattice. Crucially, we demonstrate that the fundamental mechanism underlying abrupt transitions is nucleation propagation preceded by a slow cascading process which scales with the range of dependencies. Our findings show that the synergy between these two distinct stages is essential for the occurrence of an abrupt transition. The first stage of a slow cascading mechanism was recently observed experimentally in superconducting layered materials, where heat acts as the dependency links, for the limit of infinite dependency range. Our model thus generalizes the framework to include finite dependency ranges, revealing previously unobserved mechanisms that could be experimentally verified through controlling the range of thermal diffusion in the material. As a universal mechanism, our model provides a robust method to test nucleation-controlled phase transitions in multiple systems, providing a path to discover and understand microscopic mechanisms in phase transitions.

physics.soc-ph↗

arXiv2025

From Interdependent Networks to Two-Interactions Physical Systems

Recent advances have shown that introducing dependency interactions between two superconducting networks can trigger abrupt, hysteretic normal-superconductor phase transitions. In this study, we demonstrate that such behavior can also arise in a single-network superconducting system that features two distinct types of interactions: short-range electrical connectivity and long-range thermal dependency. Using experimental and simulation methods, we show that when sufficient heat is dissipated within a single-layer disordered superconducting network, the system undergoes a mixed-order phase transition marked by both a discontinuous change in resistance and critical scaling behavior. We find that the emergence and characteristics of these abrupt transitions depend critically on the thermal conductivity of the underlying substrate, establishing heat flow as the origin of the unique phase transition. Additionally, both experimental and numerical results reveal long-lived transient states and scaling dynamics near the critical point, consistent with spontaneous branching processes observed in interdependent networks theory. These findings strongly demonstrate that complex critical phenomena, such as mixed-order transitions, previously attributed to structurally interdependent systems, can also arise within single-layer physical systems when dual interactions coexist. Our results broaden the scope of the theory and experiments of phase transitions in interdependent networks and suggest new ways to design and control phase changes in physical, biological, and technological systems where two interactions are present.

cond-mat.supr-con↗

arXiv2025

Network-driven discovery of repurposable drugs targeting hallmarks of aging

Despite the thousands of genes implicated in age-related phenotypes, effective interventions for aging remain elusive, a lack of advance rooted in the multifactorial nature of longevity and the functional interconnectedness of the molecular components implicated in aging. Here, we introduce a network medicine framework that integrates 2,358 longevity-associated genes onto the human interactome to identify existing drugs that can modulate aging processes. We find that genes associated with each hallmark of aging form a connected subgraph, or hallmark module, a discovery enabling us to measure the proximity of 6,442 clinically approved or experimental compounds to each hallmark. We then introduce a transcription-based metric, $pAGE$, which evaluates whether the drug-induced expression shifts reinforce or counteract known age-related expression changes. By integrating network proximity and $pAGE$, we identify multiple drug repurposing candidate that not only target specific hallmarks but act to reverse their aging-associated transcriptional changes. Our findings are interpretable, revealing for each drug the molecular mechanisms through which it modulates the hallmark, offering an experimentally falsifiable framework to leverage genomic discoveries to accelerate drug repurposing for longevity.

q-bio.MN↗

arXiv2025

The Spontaneous Cascading Mechanism Behind Critical Phenomena in Self-Coupled Lasers

The basic physics of lasers is characterized by a second-order continuous phase transition at the critical lasing threshold. Nevertheless, laser bistability with abrupt transitions has been reported in some laser systems, but its underlying mechanism has never been explored. Here we study experimentally and theoretically a novel nonlinearly self-coupled laser system. We show both experimentally and theoretically that this system experiences spontaneous cascading that yields an abrupt mixed-order transition. At the critical point, a long-lived cascading plateau is observed, characterized by a critical branching factor equal to one. When deviating from criticality, the branching factor departs monotonically from one. The critical scaling close to and at the critical point resembles similar phenomena observed recently in other interdependent systems, suggesting a common universal cascading origin for abrupt transitions. Our results shed light on the cascading mechanism of abrupt transitions in laser systems, which can be utilized for future research and applications.

physics.optics↗

arXiv2025

Dynamics of critical cascades in interdependent networks

The collapse of interdependent networks, as well as similar avalanche phenomena, is driven by cascading failures. At the critical point, the cascade begins as a critical branching process, where each failing node (element) triggers, on average, the failure of one other node. As nodes continue to fail, the network becomes increasingly fragile and the branching factor grows. If the failure process does not reach extinction during its critical phase, the network undergoes an abrupt collapse. Here, we implement the analogy between this dynamic and birth-death processes to derive new analytical results and significantly optimize numerical calculations. Using this approach, we analyze three key aspects of the dynamics: the probability of collapse, the duration of avalanches, and the length of the cascading plateau phase preceding a collapse. This analysis quantifies how system size and the intensity of the initial triggering event influence these characteristics.

physics.soc-ph↗

arXiv2024

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↗

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↗

arXiv2024

Nucleation phenomena and extreme vulnerability of spatial k-core systems

K-core percolation is a fundamental dynamical process in complex networks with applications that span numerous real-world systems. Earlier studies focus primarily on random networks without spatial constraints and reveal intriguing mixed-order transitions. However, real-world systems, ranging from transportation and communication networks to complex brain networks, are not random but are spatially embedded. Here, we study k-core percolation on two-dimensional spatially embedded networks and show that, in contrast to regular percolation, the length of connections can control the transition type, leading to four different types of phase transitions associated with novel phenomena and a rich phase diagram. A key finding is the existence of a metastable phase in which microscopic localized damage, independent of system size, can cause a macroscopic phase transition, a result which cannot be achieved in traditional percolation. In this case, local failures can spontaneously propagate the damage radially until the system entirely collapses, a phenomenon analogous to the nucleation process. These findings suggest novel features and extreme vulnerabilities of spatially embedded k-core network systems, and highlight the necessity to take into account the characteristic length of links when designing robust spatial networks. Furthermore, our insight about the microscopic processes and their origin during the mixed order and first order abrupt transitions in k-core networks could shed light on the mechanisms of many systems where such transitions occur.

physics.soc-ph↗

arXiv2023

Possible origin for the similar phase transitions in k-core and interdependent networks

The models of $k$-core percolation and interdependent networks (IN) have been extensively studied in their respective fields. A recent study has revealed that they share several common critical exponents. However, several newly discovered exponents in IN have not been explored in $k$-core percolation, and the origin of the similarity still remains unclear. Here, we investigate k-core percolation in random networks. We find that for k-core percolation,the fractality of the giant component fluctuations is manifested by a fractal fluctuation dimension, $\widetilde d_f = 3/4$, within a correlation \emph{size} $N'$ that scales as $N' \propto (p-p_c)^{-\widetildeν}$, with $\widetildeν= 2$, same as found in IN. Indeed, here, $\widetildeν\equiv d\cdot ν'$ and $\widetilde{d}_f \equiv d'_f/d$, where $ν'$ and $d'_f$ are respectively the same as the correlation \emph{length} exponent and the fractal fluctuation dimension observed in $d$-dimensional IN spatial networks. These two new exponents found here for $k$-core percolation demonstrate the same scaling behaviors as found for IN with the same critical exponents, reinforcing the similarity between the two models. Furthermore, we suggest that these two models are similar since both have two types of interactions: short-range (SR) connectivity and long-range (LR) influences. In IN the LR are the influences of dependency links while in k-core we find here that for $k=1$ and $k=2$ the influences are short range while for $k\geq3$ the influence is long range. In addition, analytical arguments for a universal hyper-scaling relation for the fractal fluctuation dimension of the $k$-core giant component and for IN as well as for any mixed-order transition are established.Our analysis enhances the comprehension of k-core percolation and supports the generalization of the concept of fractal fluctuations in mixed-order phase 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

Dense network motifs enhance dynamical stability

Network motifs are the building blocks of complex networks and are significantly involved in the network dynamics such as information processing and local operations in the brain, biological marks for drug targets, identifying and predicting protein complexes in PPI networks, as well as echo chambers in social networks. Here we show that dense motifs such as cliques have different stable states than the network itself. These stable states enhance the dynamical stability of the network and can even turn local stable states into global ones. Moreover, we show how cliques create polarization phenomena and global opinion changes.

physics.soc-ph↗

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↗

arXiv2022

Improving robustness of spatial networks via reinforced nodes

Many real-world networks are embedded in space, and their resilience in the presence of reinforced nodes has not been studied. Here we model such networks using a spatial network model that have an exponential distribution of link length $r$ having a characteristic length $ζ$. We find that reinforced nodes can significantly increase the resilience of the networks which varies with strength of spatial embedding. We also study different reinforced node distribution strategies for improving the network resilience. Interestingly, we find that the best strategy is highly dependent on the stage of the percolation process, i.e., the expected fraction of failures. Finally, we show that the reinforced nodes are analogous to an external field in percolation phase transition i.e., having the same critical exponents and that the critical exponents satisfy Widom's relation.

physics.soc-ph↗

arXiv2022

Percolation on spatial anisotropic networks

Many realistic systems such as infrastructures are characterized by spatial structure and anisotropic alignment. Here we propose and study a model for dealing with such characteristics by introducing a parameter that controls the strength of the anisotropy in the spatial network. This parameter is added to an existing isotropic model used to describe networks under spatial constraints, thus generalizing the spatial model to take into account both spatial and anisotropic features. We study the resilience of such networks by using a percolation process and find that anisotropy has a negative impact on a network's robustness. In addition, our results suggest that the anisotropy in this model does not affect the critical exponent of the correlation length, $ν$, which remains the same as the known $ν$ in 2D isotropic lattices.

physics.soc-ph↗

arXiv2021

Scaling of variations in traveling distances and times of taxi routes

The importance of understanding human mobility patterns has led many studies to examine their spatial-temporal scaling laws. These studies mainly reveal that human travel can be highly non-homogeneous with power-law scaling distributions of distances and times. However, investigating and quantifying the extent of variability in time and space when traveling the same air distance has not been addressed so far. Using taxi data from five large cities, we focus on several novel measures of distance and time to explore the spatio-temporal variations of taxi travel routes relative to their typical routes during peak and nonpeak periods. To compare all trips using a single measure, we calculate the distributions of the ratios between actual travel distances and the average travel distance as well as between actual travel times and the average travel time for all origin destinations (OD) during peak and nonpeak periods. In this way, we measure the scaling of the distribution of all single trip paths with respect to their mean trip path. Our results surprisingly demonstrate very broad distributions for both the distance ratio and time ratio, characterized by a long-tail power-law distribution. Moreover, all analyzed cities have larger exponents in peak hours than in nonpeak hours. We suggest that the interesting results of shorter trip lengths and times, characterized by larger exponents during rush hours, are due to the higher availability of travelers in rush hours compared to non-rush hours...

physics.soc-ph↗

arXiv2020

Epidemic spreading and control strategies in spatial modular network

Epidemic spread on networks is one of the most studied dynamics in network science and has important implications in real epidemic scenarios. Nonetheless, the dynamics of real epidemics and how it is affected by the underline structure of the infection channels are still not fully understood. Here we apply the SIR model and study analytically and numerically the epidemic spread on a recently developed spatial modular model imitating the structure of cities in a country. The model assumes that inside a city the infection channels connect many different locations, while the infection channels between cities are less and usually directly connect only a few nearest neighbor cities in a two-dimensional plane. We find that the model experience two epidemic transitions. The first lower threshold represents a local epidemic spread within a city but not to the entire country and the second higher threshold represents a global epidemic in the entire country. Based on our analytical solution we proposed several control strategies and how to optimize them. We also show that while control strategies can successfully control the disease, early actions are essentials to prevent the disease global spread.

physics.soc-ph↗