arXiv subjects

Shlomo Havlin

Publications and source records attributed to Shlomo Havlin.

At least 19 recordsLinked to original sources

arXiv2026

The role of dependency range in thermally coupled superconducting networks

Interdependent networks can be highly fragile because dependency links amplify local failures into cascades that trigger global collapse. Much of the theoretical understanding comes from geometric network models, where dependency range is imposed externally and coupling strength is distance independent, and it is unclear how these insights apply to real materials, where interdependence arises from physical transport. Here we study thermally coupled amorphous superconducting networks, in which dependencies emerge from current redistribution and heat-mediated interactions governed by thermal diffusivity. Using an experimentally calibrated model of resistively shunted Josephson junctions, we show the system reproduces the three canonical transition regimes: continuous, nucleation-dominated, and mixed-order. However, because heat-driven dependency simultaneously broadens and weakens with distance, long-range thermal coupling can stabilize the superconducting phase upon heating, in contrast with its destabilizing role in geometric models. Cascades are anisotropic, with one-dimensional front propagation. These results offer an experimentally testable perspective complementing the geometric framework.

cond-mat.supr-con↗

arXiv2026

Community-Centers Identify Robust Biomarkers in High-Dimensional, Low-Sample-Size Gene Expression Data

High-dimensional, low-sample-size bulk gene expression data poses a fundamental challenge in transcriptomics, which typically includes tens of thousands of genes but relatively few samples, leading to overfitting and unstable selection for key features as biomarkers. We propose a regression by community centers (RCC) framework tailored for such data. RCC converts gene expression data into a feature proximity network and leverages the phase transition of the giant connected component to identify a critical distance threshold that yields a sparse yet maximally informative network representation, indicated by a minimum normalized shortest compression length. Intuitively, at the criticality, the network balances fragmentation and over-connectivity, allowing meaningful gene-gene associations communities to emerge while filtering noises, such that community-centers capture the most informative and non-redundant signals. These representative genes are then fed to a simple ordinary least squares (OLS) model for downstream predictions. Across five bulk gene expression datasets, RCC displays exceptional prediction performance, clearly outperforming benchmark methods, remains robust to missing data and noises, and does not rely on external biological knowledge. These results suggest that network-based representations provide an effective and general framework for high-dimensional, low-sample-size prediction tasks beyond transcriptomics and illustrate how network-science ideas can support robust learning in data-scarce regimes.

physics.soc-ph↗

arXiv2026

Interdependent-Network Criticality without a Second Network: Cascading Collapse of the Joule-Coupled Insulator-Metal Transition in $VO_2$

When driven by a large enough electric field or current, the insulating state of vanadium dioxide ($VO_2$) typically collapses abruptly into the metallic state (the insulator-metal transition, IMT) following a brief incubation delay. These incubation delays, typically lasting microseconds or less, have been analyzed predominantly through macroscopic electro-thermal modeling, providing little statistical insight into the transition dynamics. Here, we investigate the electrically driven IMT in a two-dimensional $VO_2$ lattice and demonstrate that the material behaves as a complex, interdependent network despite being a single structural entity. Because metallic domains dissipate significantly more Joule heat than insulating regions, each switching event raises the Joule heating of the network, resulting in network dependency and triggering a cascading chain reaction. Combining time-resolved resistance measurements with a Joule-coupled resistor-network model and interdependent-network theory, we observe an abrupt (first-order) transition preceded by an anomalously long incubation plateau lasting thousands of seconds. During this metastable plateau, an effective branching factor of the switching activity approaches unity at criticality, and the plateau lifetime diverges with exponent $ζ=1/2$; the order parameter scales with exponent $β=1/2$. These signatures coincide with those predicted for interdependent networks, indicating that dissipative Joule coupling alone can drive mixed-order catastrophic cascades in a single-network correlated-oxide system.

cond-mat.stat-mech↗

arXiv2026

Catastrophic disruption cascades driven by the nonlinearity of systemic risk

Whether the COVID-19 pandemic or the Iran war, recent events have highlighted the systemic fragility of supply chains. Due to highly specific and mutual buyer-supplier dependencies, even the failure of a single firm can cause system-wide economic disruptions in the form of cascading failures up and down the supply chain network. Only recently has it become possible to quantify the systemic impact of the failure of individual firms on the total supply chain. Here, we demonstrate that the systemic risk contributions of combinations of firm failures can be drastically larger than the sum of the damage caused by the firms individually. Using a unique data set that allows us to reconstruct the national supply chain network of Ecuador at the firm-level, we find that combined failures can produce systemic risk amplifications of up to a factor of 257. However, only a tiny fraction of 0.14\% of pairs exhibit a more than 4-fold amplification of systemic risk. We develop a simple method to identify firm combinations that lead to large systemic risk amplifications. The origin of these amplifications is a breakdown of the substitutability of defaulted suppliers. We discuss the implications of the existence of rare but strong systemic risk amplification for situations that simultaneously affect multiple firms, such as natural disasters and wars.

econ.GN↗

arXiv2026

Criticality and reduced dynamical resilience in PM2.5 pollution systems

Concentration-based metrics underpin air-quality assessment, while dynamical persistence and recovery describe how rapidly high-PM2.5 episodes dissipate and how strongly they retain memory. Here we introduce a finite-memory multiplicative reversion (FMMR) process that links the lognormal concentration backbone of PM2.5 variability with event recurrence, temporal memory, variance amplification and local dynamical resilience. Across station observations and reanalysis data, elevated PM2.5 regimes show a coherent set of critical signatures: stronger memory, rising autocorrelation, broader upper tails, amplified variance, reduced resilience and more clustered exceedance events. Together, these co-occurring signals reveal dynamical criticality in PM2.5 pollution systems, with critical slowing down expressed as a loss of restoring capacity under high-pollution conditions. A gridded comparison across populated and emission-influenced regions further shows that areas with similar PM2.5 burden can differ in recovery capacity, while eastern China has shifted toward higher resilience during recent air-quality improvements and India and West Africa occupy lower-resilience states. By identifying where pollution burden and recovery capacity diverge, these findings establish dynamical persistence and resilience as complementary dimensions of PM2.5 risk and provide a quantitative basis for resilience-oriented air-quality assessment.

physics.soc-ph↗

arXiv2026

Planetary climate interactions of the Qinghai-Tibetan Plateau

The Qinghai-Tibetan Plateau (QTP), Earth's "Third Pole", profoundly shapes the Asian monsoon and regional climate and exerts far-reaching influence on the global climate system. Yet its role in organizing planetary-scale climate interactions remains poorly quantified. Here we develop a climate network framework to explicitly resolve the planetary teleconnection architecture associated with the QTP across historical observations and future climate projections, with physical consistency assessed using Lagrangian trajectory diagnostics and targeted numerical experiments. We uncover a persistent and directional interaction structure linking the QTP with multiple major climate tipping elements. In particular, we identify a robust tripolar interaction mode coupling the QTP with both the Arctic and Antarctica through coherent atmospheric-oceanic pathways. Our findings establish the QTP as a critical planetary climate integrator, revealing a significant blind spot in current climate models and risk frameworks regarding cascading tipping dynamics in a warming world.

physics.ao-ph↗

arXiv2026

Negativity Percolation in Continuous-Variable Quantum Networks

Quantum networks (QNs) have been predominantly driven by discrete-variable (DV) architectures. Yet, optical platforms naturally generate Gaussian states--the common states of continuous-variable (CV) systems, making CV-based QNs an attractive route toward scalable, chip-integrated quantum computation and communication. To bridge the gap between well-studied DV entanglement percolation theories and their CV counterpart, we introduce a Gaussian-to-Gaussian entanglement distribution scheme that deterministically transports two-mode squeezed vacuum states across large CV networks. Analysis of the scheme's collective behavior using statistical-physics methods reveals a new form of entanglement percolation--negativity percolation theory (NegPT)--characterized by a bounded entanglement measure called the ratio negativity. We discover that NegPT exhibits a mixed-order phase transition, marked simultaneously by both an abrupt change in global entanglement and a long-range correlation between nodes. This distinctive behavior places CV-based QNs in a new universality class, fundamentally distinct from DV systems. Additionally, the abruptness of this transition introduces a critical vulnerability of CV-based QNs: conventional feedback mechanism becomes inherently unstable near the threshold, highlighting practical implications for stabilizing large-scale CV-based QNs. Our results unify statistical models for CV-based entanglement distribution and uncover previously unexplored critical phenomena unique to CV systems, providing valuable insights and guidelines essential for developing robust, feedback-stabilized QNs.

quant-ph↗

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↗

arXiv2026

Long-term prediction of ENSO with physics-guided Deep Echo State Networks

The El Niño-Southern Oscillation (ENSO) is a dominant mode of interannual climate variability, yet the mechanisms limiting its long-lead predictability remain unclear. Here we develop a physics-guided Deep Echo State Network (DESN) that operates on physically interpretable climate modes selected from the extended recharge oscillator (XRO) framework. DESN achieves skillful Niño3.4 predictions up to 16-20 months ahead with minimal computational cost. Mechanistic experiments show that extended predictability arises from nonlinear coupling between warm water volume and inter-basin climate modes. Error-growth analysis further indicates a finite ENSO predictability horizon of approximately 30 months. These results demonstrate that physics-guided reservoir computing provides an efficient and interpretable framework for diagnosing and predicting ENSO at long lead times.

physics.ao-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

Stable Emotional Co-occurrence Patterns Revealed by Network Analysis of Social Media

Examining emotion interactions as an emotion network in social media offers key insights into human psychology, yet few studies have explored how fluctuations in such emotion network evolve during crises and normal times. This study proposes a novel computational approach grounded in network theory, leveraging large-scale Japanese social media data spanning varied crisis events (earthquakes and COVID-19 vaccination) and non-crisis periods over the past decade. Our analysis identifies and evaluates links between emotions through the co-occurrence of emotion-related concepts (words), revealing a stable structure of emotion network across situations and over time at the population level. We find that some emotion links (represented as link strength) such as emotion links associated with Tension are significantly strengthened during earthquake and pre-vaccination periods. However, the rank of emotion links remains highly intact. These findings challenge the assumption that emotion co-occurrence is context-based and offer a deeper understanding of emotions' intrinsic structure. Moreover, our network-based framework offers a systematic, scalable method for analyzing emotion co-occurrence dynamics, opening new avenues for psychological research using large-scale textual data.

cs.SI↗

arXiv2025

The origin of long-range links of air pollution in China

Weather conditions significantly influence the formation and dispersion of pollution variations. Here we study networks of pollution as well as climate networks and find that pollutants may not only have an impact close to their source but also show a significant correlation with pollutant concentrations thousands of kilometers away. We develop a pollution network model based on cross-correlation between PM2.5 concentration time series in different sites in China to detect stable long-range links during the last ten years. A multi-network analysis of the 500 hPa geopotential height and PM2.5 concentration suggests that long-range correlations in PM2.5 levels are also influenced by synoptic activity.

physics.soc-ph↗

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↗

arXiv2025

Unveiling the Importance of Nonshortest Paths in Quantum Networks

Quantum networks (QNs) exhibit stronger connectivity than predicted by classical percolation, yet the origin of this phenomenon remains unexplored. We apply a statistical physics model -- concurrence percolation -- to uncover the origin of stronger connectivity on hierarchical scale-free networks, the ($U,V$) flowers. These networks allow full analytical control over path connectivity through two adjustable path-length parameters, $U \leq V$. This precise control enables us to determine critical exponents well beyond current simulation limits, revealing that classical and concurrence percolations, while both satisfying the hyperscaling relation, fall into distinct universality classes. This distinction arises from how they "superpose" parallel, non-shortest path contributions into overall connectivity. Concurrence percolation, unlike its classical counterpart, is sensitive to non-shortest paths and shows higher resilience to detours as these paths lengthen. This enhanced resilience is also observed in real-world hierarchical, scale-free Internet networks. Our findings highlight a crucial principle for QN design: when non-shortest paths are abundant, they notably enhance QN connectivity beyond what is achievable with classical percolation.

quant-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↗