25,533 papers in this slice of arXiv.
Nuno Crokidakis, Celia Anteneodo
Many traditional models of electoral dynamics emphasize opinion change, social influence and persuasion mechanisms. In contrast, contemporary polarized societies often exhibit relatively stable partisan blocs coexisting with an electorally relevant mobile population. We propose a minimal dynamical model in which electoral competition is governed not by direct persuasion between opposing blocs, but by the dynamics and allocation of a mobile electoral interface. The electorate is partitioned into two stable partisan blocs and a mobile fraction, while direct transitions between the polarized blocs are strongly suppressed. Mobile voters are allocated between the competing blocs through a Fermi-like probabilistic rule governed by rejection asymmetries. Analytical calculations show that the electoral susceptibility to political shocks is proportional to the stationary size of the mobile electoral interface, identifying electoral mobility as the key quantity controlling the macroscopic response of polarized systems to external perturbations. The model naturally predicts a continuum of mobility regimes, ranging from frozen polarization to highly responsive electoral states characterized by a broad mobile interface. These results suggest that, in strongly polarized elections, aggregate electoral changes may be governed primarily by fluctuations at the mobile electoral interface rather than by large-scale ideological conversion.
Amitosh Tiwari, Chittaranjan Hens, Prosenjit Kundu
Low-dimensional reductions provide a useful framework for studying high-dimensional dynamics on complex networks, but most existing approaches are restricted to pairwise interactions. Here, we develop a one-dimensional reduction for dynamical systems on networks with purely higher-order interactions. The reduction is formulated through an effective higher-order interaction strength (βΔ), associated with the triangular interactions of the underlying network and the dynamical system's effective state. We present a theoretical framework for the dimension-reduction approach and validate it across three dynamical models with exclusively higher-order interactions. We find that the reduction accuracy is mainly determined by the homogeneity of node states, i.e., the deviations in state values become very small. Numerical results on synthetic and real networks show that the reduced model captures the effective steady states and transitions of the full system with good accuracy.
Jacob Haqq-Misra, Ravi Kopparapu
This paper analyzes the 112 sensor videos released by the U.S. Department of War under the PURSUE (Presidential Unsealing and Reporting System for UAP Encounters) initiative. Redactions to the on-screen displays limit the information available for identifying the nature of the reported unidentified anomalous phenomena (UAP). The rate at which any object transits across part or all of a frame can be measured in pixels per frame; however, converting this to a physical velocity also requires (1) the range to the object, (2) the velocity of the observing aircraft, (3) the aspect angle between the object's path and the observer, and (4) the field angle of the camera sensor. No sensor video in the PURSUE corpus provides this complete set of information. In one clip (DOW-UAP-PR113), the object crosses visible depression-angle markings, allowing the camera's field of view to be reconstructed. Even this clip does not provide sufficient information to distinguish between a bird flying past the sensor at close range and a large craft traveling at Mach 5 a kilometer away. A second clip (DOW-UAP-PR149) includes a vessel of known size in the frame, which can be used as a reference to calculate an upper bound of Mach 0.4 on the object's relative velocity. Without further information, the PURSUE sensor videos in their present form cannot fully resolve unidentified cases or conclusively indicate anomalous velocities.
Wei-Peng Nie, Xiao-Yong Yan, Bin Jia +4
Urban mobility promises social integration, yet daily movement is systematically constrained by socioeconomic hierarchies. Introducing "economic distance"--the continuous income gap between origin and destination--as a unified lens, we analyze large-scale mobility records across 109 U.S. cities to reveal how urban flows are structured. We identify a universal structural boundary: flows concentrate intensely within a narrow economic distance of 0.25 quantiles, defining the effective "economic radius" of routine mobility. This boundary exhibits profound asymmetry; upward mobility faces a uniform structural ceiling across cities, whereas downward mobility drives cross-city heterogeneity. Mechanistically, the boundary is physically anchored by meso-scale residential clustering but is further tightened by an independent economic-distance friction, validated via gravity modeling. These interactions yield four distinct mobility regimes, with "affluent-confined" systems exhibiting the strongest stratification. These findings establish economic distance as a fundamental, asymmetric, and multi-scale filter shaping urban inequality, offering new theoretical grounds for interventions targeting structural barriers to cross-class interaction.
Juergen Renn
Designs for international climate cooperation face a trade-off between allocative efficiency and robustness to the erosion of institutions by defection, renegotiation, and political turnover. We formalize this trade-off in a stylized coalition-formation game in which membership is driven by two market-based channels, a membership premium and an outsider drain, and is stabilized against a bounded set of institutional perturbations using the apparatus of robust control. The free-rider gap that each member faces is derived from economic primitives, the carbon price differential and the emission intensity of exports, and separated from the architecture-specific channels; the separation makes the model solvable and comparative. The model is bistable: a small remnant club and a near-universal coalition are separated by a critical mass. The established coalition withstands roughly twelve times the perturbation admissible at ignition, and the tipping survives heterogeneity up to a critical disorder of about seven times the membership premium. Two robustness coordinates, an ignition cost and a collapse threshold, place each entry architecture in a map with three regimes: self-igniting, founding-dependent, and permanent-support-dependent. The carbon currency, a newly proposed quantity-based system whose emission rights are reissued each period, extinguished upon use, and enforced at the border, is self-igniting; the border adjustment is founding-dependent; the export rebate is permanent-support-dependent. Architectures without an outsider drain, such as clean-up certificates and emissions-trading-system linking, form a separate class that improves efficiency within a coalition but cannot drive its formation. Robustness governs whether cooperation forms and endures; efficiency decides only how much an established coalition delivers.
Kartik Dahake, Abhijit Chakraborty
Signed graphs provide an effective architecture for portraying a system in which cooperation and conflict coexist. Emerging from the concept of balance in psychological sciences, they have found applications across several domains. Financial markets are one such example that can be modeled using signed networks, where assets exhibit correlations in price movements. During periods of systemic risk, such a signed financial network shows a loss of balance, which has been consistently demonstrated. Here, we explore how this structural imbalance is distributed across scales within the financial network, revealing its mesoscopic origin. Adopting the framework of structural balance theory, we use a measure of polarization based on triadic motifs to investigate the distribution of structural imbalance across varying sectoral scales. We analyze the temporal evolution of global polarization and its sectoral constituents using longitudinal data derived from the S&P 500 index. By decomposing global polarization into intra-sectoral and inter-sectoral constituents, we show that structural imbalance arises predominantly from interactions between sectors rather than within them during periods marked by systemic risk. We employ randomization protocols to confirm that observed imbalance configurations are statistically significant and not artifacts of lower-order interactions. We derive a regression equation demonstrating that the variance in global polarization is well explained by macroeconomic variables, indicating that low levels of global polarization during economic crises are driven by compounding pressures from supply chain disruptions and inflation uncertainty. Collectively, these findings provide a quantitative framework for understanding how localized sectoral conflicts propagate across the financial network and contribute to large-scale structural instability during periods of economic crisis.
Eiichi Shoji
The 2024 Noto Peninsula Earthquake caused widespread power outages, communication failures, and road disruptions, and a tsunami warning was issued immediately after the earthquake, highlighting the importance of reliable disaster information transmission. The Noto Peninsula is surrounded by the sea on three sides and has limited transportation networks, making community isolation and securing reliable information transmission critical challenges. This study defines "Disaster Information Reachability" as the capacity to deliver necessary information to those who need it when they need it, and examines the role of medium-wave AM broadcasting from this perspective. In September 2024, the author conducted a mobile reception survey from Kanazawa to Suzu City and a fixed-point reception survey in Suzu City using commercially available radio receivers and passive radio receivers, including HOOPRA. Medium-wave stations in Niigata, Toyama, Akita, and Tokyo were clearly received, although the local NHK Kanazawa stations were affected by noise. The nationwide high-power NHK Radio 2 network, which was still operational at the time, was confirmed to provide a wide-area, multidirectional reception pathway through maritime propagation, demonstrating its critical significance as a redundant disaster information infrastructure. The fact that FM relay station damage was documented in subsequent policy materials, while medium-wave AM broadcasting received no explicit attention, suggests that its disaster information reachability remains insufficiently recognized. Medium-wave AM broadcasting, with its wide-area simultaneous broadcast capability, high-power coverage, and maritime propagation characteristics, functions effectively as a disaster information infrastructure in peninsular regions and warrants reassessment in preparation for large-scale disasters such as a Nankai Trough megaquake.
Md Rafid Islam, Rafsan Jany, Md. Siyam Bhuiyan +3
Air pollution is one of Bangladesh's most pressing environmental problems, yet few studies have examined it at a national scale over a long time span. To describe trends, seasonality, geographic patterns, and pollution regimes, this study examines 25 years of hourly air quality data (2000-2025), covering over 3.19 million records, eight pollutants, and 103 cities monitored from 2022 onward following expansion from a single Dhaka station. The composite AQI series showed no significant long-term trend, an artifact of this network expansion rather than a real flattening of pollution, since Dhaka's own AQI rose steadily before the network grew. Seasonality was strong and highly significant, as AQI peaked in January (162.26) and fell to its lowest in July (61.47), tracking the dry and monsoon seasons. Geographically, southeastern coastal cities, including Teknaf and Bandarban, consistently recorded the lowest AQI levels. Dhaka remained the most polluted city, and air quality improved progressively with distance from the capital. K-means clustering of city-level AQI trajectories identified four distinct pollution regimes, ranging from cleaner coastal regions to a rapidly deteriorating Dhaka-Narsingdi cluster with an average increase of 1.84 AQI per year. Correlation analysis showed that PM2.5 and PM10 were the dominant contributors to AQI variation, and so combustion-related emissions are the primary target for air quality management and policy intervention.
Yun-Long Zhang, Jia-Ning Kang, Xiaoming Kan +5
Decarbonizing existing coal-fired power plants can contribute to near-term climate mitigation, but identifying cost-effective retrofit strategies is complicated by interactions among mitigation technologies. Here we develop an interaction-aware optimization framework that jointly evaluates energy conservation, biomass co-firing, and carbon capture across 1,885 coal-fired power plants in China while accounting for plant heterogeneity and shared biomass and CO2 storage resources. We find that technology interactions alter both mitigation costs and the emission reductions attributable to individual measures, thereby changing cost-optimal technology portfolios and marginal abatement cost curve at the fleet level. Approximately 1.2 Gt CO2 yr-1 can be mitigated at negative marginal cost, while reaching carbon neutrality requires a marginal abatement cost of US56 t CO2-1. Progressively deeper mitigation shifts the cost-optimal portfolio from energy conservation toward biomass co-firing and ultimately carbon capture, with biomass combined with carbon capture enabling net-negative emissions. Explicitly accounting for interactions among mitigation technologies therefore provides a more consistent basis for evaluating coal-power decarbonization and coordinating retrofit investment, infrastructure development, and climate policy.
Yun-Long Zhang, Jia-Ning Kang, Lan-Cui Liu +3
New coal-fired capacity may still be needed in some economies to support energy security, yet tightening carbon constraints increasingly threaten its long-term investment viability. Designing new plants to be Carbon Capture, Utilization and Storage (CCUS)-ready is therefore critical, but existing investment studies largely neglect the spatial factors that shape CCUS investment viability. In this study, we propose a Spatial Real Options (SRO) framework that couples national-scale siting screening with site-level real-options valuation. A national inventory of technically feasible sites across China (n = 194,027) is linked to a real-options model that incorporates temporal uncertainty together with province-specific conditions, CO2 transport distance, and alternative policy instruments. Results show that the nationwide investable window for conventional coal closes by the early-to-mid 2040s. CCUS integration materially reshapes project economics, but outcomes differ substantially across storage options and transport distances: oil-reservoir storage remains economically attractive at short distances, whereas saline-aquifer storage requires operational incentives to become competitive. Generation-hour compensation substantially outperforms investment-cost subsidies, indicating that operating costs, rather than upfront capital, constitute the binding constraint. The resulting spatiotemporal maps provide a practical decision-support tool for identifying where and when CCUS-ready coal investments are most attractive under progressive decarbonization.
Ruben E. Araújo
Do social-media platforms polarize because they hide disagreement, or because they expose people to it? We study a continuous-time model in which agents diffuse in physical space by Brownian motion while interacting through an adaptive, algorithmically curated digital network under a finite attention budget. When influence is purely assimilative (bounded confidence), opinion-blind long-range exposure heals locality-induced fragmentation and algorithmic homophily builds echo chambers. Once influence includes a repulsive response to strongly opposed views, the ranking of platform designs inverts: a neutral, uncurated platform drives maximal polarization with opinions pinned at the extremes; a controversy-seeking engagement algorithm is nearly as radicalizing; and strong algorithmic homophily paradoxically shields the population from extremism. A two-bloc reduction shows that the stationary cross-bloc attention fraction set by the engagement kernel controls the radicalization rate and a finite-horizon crossover λc(T) in the digital attention share---consistent with simulations, with no parameters fitted to the onset data. Radicalization is thus rate-limited, not threshold-limited. Across repulsion parameters, kernels, and sizes up to N=1600, uncurated exposure sets the saturation level of radicalization. A mobility--attention phase diagram shows that once the platform owns most of an agent's attention, physical mobility becomes irrelevant. Under opinion-independent mobility we detect no geographic opinion structure; a Schelling-type homophilic drift of strength χ restores it only above a Péclet threshold χℓ/D∼1. The model offers a mechanism for field observations in which curated cross-cutting exposure increased polarization, and implies that exposure-diversity interventions can have either sign depending on the prevalence of negative influence.
Jaesung Kim, Changhee Cho, Jae Woo Lee
This study investigates whether the macroscopic statistical maturity of cryptocurrencies implies dynamical equivalence with traditional equity markets. We analyze high-frequency data (2020--2025) using the Complexity--Entropy Causality Plane (CECP) and directed horizontal visibility graphs (directed HVG) to uncover complex temporal patterns and time-directed structures in the return series. While conventional stylized facts show striking convergence across all assets, structural diagnostics reveal a compelling paradox: cryptocurrencies appear more locally random than the equity benchmark during ordinary periods, yet exhibit significantly stronger directional time-irreversibility around high-visibility return events. The absolute-return results show that large cryptocurrency fluctuations tend to begin abruptly and remain elevated afterward. Separate analyses of positive returns and negative-return magnitudes show that this pattern is shared across cryptocurrencies on the upside but varies across assets on the downside. We conclude that statistical maturity is only skin-deep; the underlying dynamical processes of mature cryptocurrencies remain fundamentally distinct from traditional benchmarks.
Alberto Acedo
The Triadic Stress Index (TSI) takes a network index whose four factors were first observed in soil microbiome co-occurrence networks and applies it, without alteration, to the correlation network of financial assets. We test it on five markets spanning 2006-2026 (equities including banking crises and the AI sector, cryptocurrencies, commodities, foreign exchange and sovereign debt), against three independent definitions of a crisis episode, at a fixed alarm budget, out of sample, with block-bootstrap intervals and a Holm correction across the family of tests. The benchmarks are the Absorption Ratio, the industry standard used by MSCI and central banks; the effective rank and the Vendi score, the sharpest spectral measures available; Ollivier-Ricci curvature; and the global and local balance indices of signed correlation networks. Three comparisons favour the index. It carries a per-node decomposition, diag(A^3), naming which asset is carrying the concentration with no parameter to select, and scores 0.97-0.99 against 0.33-0.84 for the only published per-node alternative, whereas spectral attribution must first choose how many components to read and collapses under a standard but wrong choice. Its alarms are the cleanest of anything tested, 4.0% of them with no matching episode against 14.7% for the effective rank and roughly 59% for the Absorption Ratio. And it beats the Absorption Ratio on detection by 0.273 in F1 out of sample, p<0.0005. The remaining comparisons are ties. Against the effective rank and the Vendi score the index ties in every scheme and both samples, and the margin over the Absorption Ratio narrows under the strictest labelling. On real matrices the far simpler node degree reproduces the attribution. A lead-lag analysis puts the peak cross-correlation at zero lag: this is a coincident state index, not a forecast.
Yu Tian, Eleanor Wiesler, Melanie Weber
Understanding the geometry of complex networks is critical for effective modeling and analysis across domains. While discrete notions of Ricci curvature have emerged as powerful tools for characterizing both local and global network structure, existing formulations are largely confined to undirected networks with real-valued weights. This limits the use of curvature-based analysis of directional and complex-weighted relations that arise naturally in many applications, from social and biological systems to quantum and signal-processing networks. In this work, we introduce a principled extension of Ollivier's Ricci curvature to complex-weighted graphs, which encompasses directed graphs as a special case. We establish fundamental theoretical properties of this new notion, including relations to the magnetic Laplacian and combinatorial upper and lower bounds that relate curvature to cycle structure in local neighborhoods. We further develop computational methods for curvature estimation and demonstrate their utility in community detection on directed networks.
Chirag Sharma, Shantanu Desai
We examine whether the citations received by refereed astronomy papers vary systematically with the day of the week of their arXiv preprint release. We use NASA ADS citation data for all refereed articles published in 2020--2023 in six journals (MNRAS, ApJ, ApJL, ApJS, A&A and JCAP; 37,173 papers with astro-ph primary preprints), combined with version-1 submission timestamps from the arXiv API. We group papers both by submission weekday and by the day their announcement batch appears on arXiv, accounting for arXiv's weekday deadline and lack of weekend announcements. Given the heavy skew of citation distributions, our primary statistic is the median citations per group, with bootstrap confidence intervals. Papers submitted on weekends, all of which appear in the Monday listing, have median citations significantly lower than weekday submissions in five of the six journals, by 12--26 %; the sixth (JCAP) shows the same direction, but not significantly, consistent with its small weekend sample. Median citations are indistinguishable across the five weekdays. These results extend the recently reported weekend citation disadvantage in particle and nuclear physics to astronomy. The link is correlational, consistent with either compositional variations in weekend submissions or reduced visibility in the enlarged Monday listings.
Yunhao Ding, Andreas Münch, Renaud Lambiotte
Traffic-induced failures, from packet loss in communication networks to congestion breakdown in transport systems, occur when flows progressively exhaust the edges they traverse. Path percolation models this process by removing edges along sampled origin-destination paths. Existing work assumes locally tree-like networks and deterministic shortest-path routing, leaving unclear how path degeneracy and routing stochasticity affect fragmentation in the clustered networks typical of real systems. We introduce a generalised path-percolation framework where paths are drawn from a temperature-controlled routing ensemble interpolating between geodesic and noisy transport. We argue based on box-covering renormalisation and our numerical experiments that, for any finite routing horizon C, the process coarse-grains to ordinary mean-field percolation. Routing details affect non-universal quantities, especially the percolation threshold pc, through the entropy of the load distribution and the capacity of finite clusters to accommodate flow. Load entropy therefore acts as a robustness measure for networks under path-based failures. When the routing horizon is tuned to the mean-field correlation length, C=N1/3, within a source-uniform ensemble, the system enters a crossover regime with scaling exponents distinct from shortest-path percolation with infinite budget. In this regime, path elongation becomes decoupled in time from structural fragmentation: the characteristic path length reaches a growing maximum, associated with routing temperature, asymptotically ahead of the collapse of the giant component. These results clarify how microscopic routing organisation shapes macroscopic resilience, and identify path elongation as a measurable precursor of failure in communication and transport infrastructure.
Ignacio M. Sticco
Reggaeton is one of the most widely consumed music genres in the world, and its lyrics are commonly regarded as highly sexualized. This claim rests mostly on qualitative studies and on small-scale quantitative ones. This paper has two goals. First, we present a reproducible method that uses a large language model to quantify thematic content in song lyrics along several independent dimensions. The method is not restricted to sexual content. Second, we apply it to a corpus of 1,259 songs by 12 reggaeton artists released between 2002 and 2025. The analysis covers four topics: a dataset characterization, a per-artist comparison, an analysis of how the dimensions change over time, and a comparison between our sexual-explicitness score and Spotify's own explicit flag. We release the data collection code, the scoring prompt, and the corpus, so that other researchers can replicate the approach or apply it to their own lyrics datasets.
Peter Mann, Simon Dobson
Message passing is exact for progressive epidemics on trees: infection is unidirectional, and a node's neighbours are uncorrelated in the cavity graph. A recurrent disease breaks this even on a tree due to backtracking. A node infects a neighbour, recovers, and is reinfected by it, so infection traverses an edge in both directions. Unrolled along a time axis, the outward and return transmissions together with the node's own persistence in time form a closed cycle in the space--time graph, correlating the states that ordinary message passing assumes independent. We treat these correlations by solving the dynamics exactly inside a ball of radius d about an edge and closing the ball's boundary with a single conditional per-edge message. For fixed-period susceptible--infected--susceptible dynamics this generates a hierarchy of closures on the endemic state indexed by d, in which the ball of radius d treats exactly every space--time cycle of spatial reach at most d and retains the remainder only through the mean rate supplied by its boundary. Linearising the resulting message map gives the endemic threshold, with an accuracy that increases with d. We compare the hierarchy against Monte Carlo simulation on random and empirical networks, finding excellent agreement.
Longzhao Liu, Zhihao Han, Xingru Chen +4
Curbing harmful information contagion remains a critical challenge, motivating platform-level interventions such as group dissolution to sever transmission chains. However, in practice, users affected by dissolution often exhibit adaptive behavior, rewiring to form new groups. Yet, it remains unclear how these two mechanisms jointly shape information contagion and whether group dissolution remains effective in suppressing it. Here, we develop an adaptive higher-order contagion model that integrates platform-induced group dissolution with user adaptive rewiring, and derive a theoretical framework. Notably, we reveal an effective window for group dissolution, bounded by a critical infection rate. Above this threshold, dissolution backfires and amplifies information prevalence. Within this window, dissolution acts non-monotonically, initially exacerbating prevalence before eradicating contagion via a discontinuous transition beyond a critical dissolution rate. We further show that higher-order reinforcement expands this infection-rate window over which dissolution remains effective, whereas rewiring homophily substantially narrows it. Simulations on empirical hypergraph also validate these findings. Our work highlights the interplay between top-down platform interventions and bottom-up user adaptation, underscoring the need to account for adaptive responses when designing strategies to curb harmful information without unintended amplification.
Gary Charness, Francesco Feri, Matthew O. Jackson +2
We provide a first causal analysis of the behavioral consequences of the friendship paradox-the fact that people's friends in a network have more connections than average. We find that people's behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Moreover, we find that they fail to learn to overcome such a bias when relocated within the network, varying their observational environment. In these games of complements, the friendship paradox generates a systematic upward distortion in actions, increases behavioral dispersion, and persists despite learning opportunities.