Network epidemic simulation enables fine-grained understanding of epidemic behavior. However, empirical samples of interaction networks display properties that are challenging to capture with popular synthetic models of networks. Our empirical results show that epidemic spread behavior is sensitive to a form of multi-scale local structure that is absent in common baseline models, (e.g., Erdős–Rényi, Chung-Lu, etc). This structure critically impacts the effect of local quarantining and stops epidemic spread in samples of interaction networks, even when it cannot be halted in simple synthetic models of those networks. Insights from our analysis include how epidemics on networks with widespread multi-scale local structure are easier to mitigate, as well as characterizing which nodes are ultimately not likely to be infected. We demonstrate that this structure results from more than just local triangle structure in the network, and we illustrate processes based on homophily or social influence and random walks that suggest how this multi-scale local structure arises and use it to cleanly isolate intervention sensitivity to multi-scale local structure.
Graph neural networks learn from relational structure but can be sensitive to edge-level noise. We study a hybrid graph classifier that combines a message-passing branch (Graph Isomorphism Network, GIN) with a topological branch based on extended persistence diagrams and PersLay embeddings. Training optionally uses a paper-specific hinge penalty motivated by stable persistence-diagram representations and Lipschitz regularity, which we abbreviate as “HK-inspired.” On six TUDataset benchmarks, we compare five ablations (Full, GIN+PersLay, GIN + HK, GIN only, PersLay only) and measure robustness as accuracy drop under 10% random edge removal at test time, with persistence diagrams fixed from the original graphs; we also report targeted perturbations on MUTAG and PROTEINS and runtime on all six datasets. The regularized configurations reduce relative accuracy degradation where GIN-only is sensitive, particularly on smaller molecular and protein graphs, but do not consistently maximize clean or perturbed accuracy. On large social-network benchmarks, robustness differences are small and clean accuracy is the main differentiator. Adding PersLay to GIN can improve accuracy on several datasets, while PersLay alone performs worst. Training the full model increases per-epoch cost relative to GIN-only, with most overhead during training rather than inference. Overall, the results support a conditional empirical conclusion: combining structure, topology, and stability-oriented training can improve robustness under specified structural perturbations, with dataset-dependent accuracy–stability trade-offs rather than universal guarantees.
Livestock diseases such as Rift Valley Fever (RVF) present systemic threats to animal health, rural livelihoods, and national economies in endemic regions. However, conventional impact assessments often fail to capture the nonlinear feedbacks, time lags, and cross-sectoral interactions involved. This study develops a dynamic, multi-component system dynamics (SD) model to simulate the Epidemiological and Economic impacts of RVF outbreaks in Ijara County, Kenya, under alternative vaccination strategies. The model integrates biologically detailed Susceptible Exposed Infectious (SEI) dynamics for Aedes and Culex mosquito vectors, Susceptible Exposed Infectious Recovered (SEIR) livestock infection dynamics, herd demography, and end-market processes, incorporating demand-side shocks and behavioral feedback. Using a 10-year simulation with a daily time-step, parameterized with primary and secondary data, including El Niño associated rainfall patterns and producer survey results, the model reproduces key outbreak features consistent with historical RVF events and enables ex-ante comparison of policy responses. Findings indicate that the business-as-usual (BAU) strategy, involving delayed reactionary vaccination, yields only marginal improvements in herd recovery and producer income relative to no intervention. In contrast, annual preventive vaccination with sufficient coverage of susceptible animals prevents simulated outbreaks within the model horizon, while biannual and triennial strategies reduce outbreak severity but do not fully eliminate risk. Shortening the delay between outbreak onset and vaccination initiation substantially reduces livestock losses and improves income recovery trajectories. These results highlight the value of system dynamics modeling for evaluating intervention strategies under uncertainty. The model offers a decision-support tool for livestock health policy, demonstrating that proactive vaccination and rapid response outperform delayed, reactive approaches in both disease control and economic resilience in RVF-prone pastoral systems.
This study develops the novel SVAITRS-B model, unifying deterministic and stochastic frameworks to capture cholera dynamics with vaccination, asymptomatic carriers, and environmental pathways. We demonstrate analytically that person-to-person transmission exhibits backward bifurcation, while environmental transmission follows classical forward bifurcation-establishing distinct elimination thresholds that explain disease persistence even when the basic reproduction number ℛ 0 D falls below one. Stochastic simulations reveal that human-contact transmission generates 30% greater outbreak variability than environmental routes, highlighting its role in unpredictable epidemics. Environmental transmission, however, dominates long-term endemicity, contributing 68% to ℛ 0 D . We identify critical hysteresis effects governed by vaccine efficacy ( f V ) and bacterial shedding ( ξ A , ξ I ), and uncover a logarithmic sensitivity of bacterial concentration to sanitation-indicating that standard intervention targets may underestimate effort by 15–20%. These results provide a mathematical foundation for dual-pathway control strategies, combining human-focused interventions with environmental management. Our accompanying computational toolkit enables scenario testing for public health planning, though field validation of spatial heterogeneity remains essential for localized application.
Gene regulatory networks (GRN) control the expression levels of proteins in cells, and understanding their dynamics is key to potentially controlling disease processes. Steady states of GRNs are interpreted as cellular phenotypes, and the first step in understanding GRN dynamics is describing the collection of steady states the network can support in different conditions. We consider a collection of all monotone Boolean function models compatible with a given GRN, and ask which steady states are supported by most models. We find that for balanced networks, there is an explicit hierarchy in the prevalence of individual steady states, as well as the prevalence of bistability and multistability. The key insight that we use is that monotone Boolean models supporting a given equilibrium are a product of prime ideals and prime filters of the lattices of monotone Boolean functions. To illustrate our result, we show that in the EMT network associated with cancer metastasis, the most common equilibria correspond to epithelial (E) and mesenchymal (M) states, and the bistability between them is the most common bistability among all network-compatible monotone Boolean models.
Social psychology theories of collective action argue that shared social identity mediates coordinated behavior through interacting collective mental states, such as the perception of a common grievance, the corresponding corrective action norms, and the associated efficacy beliefs. Large-scale social media data make it possible to test these theories quantitatively, but operationalizing them in online settings remains challenging. We propose an operationalization that maps the theoretical mental states onto discourse and evaluates their temporal interdependence using trend analysis, stationarity tests, vector autoregression, and pathway analysis. We apply this framework to the communal discussion of alleged voter fraud in the ∼90 million social media posts around the 2020 and 2024 U.S. presidential elections. In 2020/2021, we observe a canonical interaction sequence that the theories suggest: grievance about the alleged electoral fraud predicts subsequent mutual validation, validation predicts shared identity, and identity predicts efficacy beliefs and action discourse. Overall, the results are consistent with a collective psychological alignment that strengthens in the runup to January 6, 2021. Conversely, the 2024/2025 results do not reliably support either the alignment or the canonical sequence. Instead, the relationships are often negative or weak: grievance can suppress efficacy, action can reduce grievance, and identity predicts validation without consistently predicting action. These contrasts show that the coupling among grievance, validation, identity, efficacy, and action in digital conversation is context-dependent rather than universal. By operationalizing psychological theories in online discourse, the study both confirms specific theoretical mechanisms behind collective action in one electoral context and identifies the conditions under which the mobilizing alignment fragments in another.
Computational modeling and quantifying the drivers of modern political campaign finance is an emerging area of interest among researchers, policymakers, and the general public alike. In a federal legislative body like the U.S. House of Representatives, campaign finance decisions or “money flows” between party members are legal and common practice among both political parties for allocating resources and influence. Using extensive data from the Federal Election Commission, we model these money flows as complex networks and explain their formation using exogenous factors, previously only discussed qualitatively, such as seniority, non-coordinated SuperPAC expenditures, and House leadership status. Our results show that these factors have significant and persistent effects on both parties. These findings provide an empirical basis for ongoing debates about term limits in Congress and the role of unlimited independent expenditures by SuperPACs.
Unlike schools of fish or flocks of birds, social bats who use echolocation for navigation are faced with the complex challenge of signal processing while living and flying in groups. Interference between an individual’s calls and those of its conspecifics can be confused, making these signals difficult to study and interpret for both animals and researchers. Moreover, it is not necessarily clear from the literature what form models that seek to predict bat echolocation behaviour should take. For example, we know that bats change their calls when flying in groups, but the literature is not consistent on what specific changes are implemented. Here, we collect and analyse data from a colony of wild gray bats, Myotis grisescens , to explore whether the number of bats emerging from the roost at a given time and the physical geometry of their environment influences their echolocation calls. Specifically, we explore whether the number of calls and their acoustic power are influenced by the group size and presence of obstacles using a time-series analysis tool called transfer entropy (TE). We further apply a data-driven algorithm called sparse identification of non-linear dynamics (SINDy) to describe a model for this phenomenon. Our findings reveal significant TE values, indicating that call properties are influenced by both social and environmental factors, without assuming a form for this influence. Additionally, SINDy-based models suggest how bats adjust their echolocation behaviour in response to the presence of conspecifics and environmental obstacles. These results provide new insights into the role of sociality and physical surroundings in shaping echolocation dynamics in gray bats.
Transportation accounts for nearly one quarter of global greenhouse gas (GHG) emissions. A significant proportion of transportation emissions can be attributed to supply chain transport, which also represents the fastest-growing sector of emissions. As a way of addressing this challenge in the effort to combat global climate change, many local and national governments have leveraged public policy in the form of carbon taxes, emissions trading systems (ETSs), and subsidies for heavy goods electric vehicles (HGEVs). Firms affected by these policies are thus faced with higher costs for more emissions-intensive supply networks and a lower barrier to entry towards adopting HGEVs. However, the exact policy conditions under which firms would be most motivated to change their behaviors remains unclear. In this paper, we develop a novel methodology to address this obstacle in the form of a bi-objective green vehicle routing problem. The first objective is the minimization of the total cost of transportation over a set of vertices comprised of a depot, customers, and charging stations; the second objective is the minimization of total GHGs emitted during transportation. The proposed approach considers the three policy instruments and their effects on both fleet mix decisions (i.e., the conditions under which a firm is most motivated to adopt HGEVs) and cost- and GHG-minimizing routing options. Via an analysis of the change of the Pareto frontier given increasingly stringent carbon pricing and/or increasingly generous HGEV subsidies, firms may consider routing options that yield the most significant GHG emissions reduction at the lowest cost. To this end, we provide a survey of current and forecasted global trends related to carbon tax rates, ETS carbon allowance prices, and HGEV subsidy amounts.
Mobility networks are vital for economic activity, social interaction, and urban development, yet they remain highly vulnerable to external shocks. The COVID-19 pandemic profoundly disrupted human mobility, but most studies have focused on short-term responses or macroscopic patterns, leaving long-term structural transformations underexplored. Here, we analyze Mexico’s intermunicipal mobility network from 2020 to 2021 using a mesoscopic decomposition framework that distinguishes local (short-distance) components from global (long-distance) connections. This multiscale approach moves beyond static or node-level metrics to reveal how connectivity itself was reshaped. Clustering analysis and change point detection further uncover temporal shifts in mobility dynamics. Our results show three clear phases. Before the pandemic, the network was dense and highly connected. During the pandemic, mobility fragmented into smaller, locally cohesive clusters, reflecting sharp declines in long-distance travel. After restrictions eased, mobility partially recovered but never fully returned to its pre-pandemic structure, indicating lasting behavioral and structural shifts. Regional disparities were pronounced: western and northwestern regions showed greater resilience, while southeastern regions remained fragmented longer. Broader lifestyle changes—including remote work, digitalization, and e-commerce—reinforced local clustering and weakened interregional ties, pointing to a durable reconfiguration of mobility networks. By integrating a temporal, multiscale perspective, this study reveals how crises reshape both local cohesion and interregional connectivity. Beyond documenting disruption, it shows that mobility systems do not simply “bounce back.” Instead, they reorganize, often unevenly, underscoring the urgency for adaptive transport policies, resilient urban planning, and digital infrastructure capable of supporting mobility in a permanently altered landscape. These insights provide a data-driven foundation for future mobility resilience strategies.
Computational modelling of dynamical systems often involves many free parameters estimated from experimental data. The information gained from an experiment plays a crucial role in the goodness of predictions and parameter estimates. Optimal Experiment Design (OED) is typically used to choose an experiment containing maximum information from a set of possible experiments. This work presents a novel Bayesian Optimal Experiment Design Selection principle for generalised parameter distributions. The generalization is achieved by extending the β -information gain to the discrete distributions. The β -information gain is based on what is known as the Bhattacharyya coefficient. We show that maximising the β -information gain is equivalent to maximising the angle between the prior and posterior distributions. We analytically show, with uniform prior, selecting an experiment that maximises β -information gain reduces the posterior’s uncertainty. Further, we apply the proposed experiment selection criteria for two realistic experiment designs in systems biology. Firstly, we use the β -information gain to choose the best measurement method for parameter estimation in a Hes1 transcription model. The measurement method selected by the β -information gain results in the minimum mean square error of the parameter estimates. In the second case, we employ the proposed information gained to select an optimal sampling schedule for the HIV 1 2 LTR model. The sampling schedule chosen by the presented method reduces both prediction and parameter uncertainty. Finally, we propose a novel method for model selection using β -information gain and demonstrate the working of the proposed method in the model selection in compartmental models.
Thomas Schelling introduced his agent-based model of segregation in 1971 and concluded that even when there is a low amount of intolerance within society that segregation will develop if people follow their individual preferences. A large body of literature building of this framework has been built and has bolstered this claim. This paper aims to take the same framework but instead look for ways to get to an integrated state. We focus on Allport’s contact hypothesis that states that if there is equal status among groups, common goals among groups, and an institutional mechanism supporting intergroup contact then intergroup contact can reduce prejudice. We incorporate the contact hypothesis by having individuals adjust their intolerance based on their current neighborhood composition and the ease of conforming to their surroundings. Furthermore, we add in positive and negative media effects, as individuals are likely to get information about an outgroup from the media (e.g., news, TV, movies, etc.) that they consume. We find that having a society composed of individuals who do not easily conform to their surroundings and displaying positive examples of both groups in media promote integration within society.
Social learning is important to humans and other animals as they gather information about their environment. Information and behaviours can therefore spread rapidly through social networks as contagions. However, the way individuals acquire and use social information is highly variable and frequently complex, often shaped by higher-order or multibody interactions that are not straightforwardly described by conventional dyadic networks. There has been considerable recent progress in modeling social contagions across higher-order networks that explicitly quantify these multibody interactions. A challenge for studying social contagion across multibody or higher-order interactions is the diversity of ways in which knowledge can be exchanged within or among groups. Here we provide a typology of knowledge exchange rules in higher-order networks, focusing on both learning and discovery. We also provide a non-exhaustive list of many basic knowledge exchange rules, to demonstrate our typology and its value in distinguishing between different mechanisms of social learning. Our aim is to provide a framework that helps researchers interested in modeling knowledge exchange in higher-order networks to develop new models and adapt existing models to questions of interest. By doing so we hope to promote interdisciplinarity in the study of how multibody interactions shape social contagions, especially at this critically incipient stage - avoiding the inevitable challenges from the eventual need to integrate parallel, independent, complementary advances among different disciplines.
Substantial policy efforts to develop regional innovation systems (RIS) highlight the importance of understanding the institutional factors that promote integration and synergy at the regional scale. To this end, we analyzed historical patterns of research co-production within and across California (CA) and Texas (TX), two US regions that account for >5% of global research publication. This predominance is largely attributed to the University of California and the University of Texas, two multi-campus university systems (MUS) that feature distinct configurations of institutional research specialization. We exploit these differences to analyze four institutional assortativity channels that foster RIS synergy: institutional proximity, prestige, homophily, and specialization. Descriptive analysis reveals that institutional co-publication rates differ within and across RIS and are influenced by external socio-economic shocks, such as the 2007-08 financial crisis, which intensified institutional clustering within these research university ecosystems. We also develop institutional specialization profiles for exploring the structure and role of institutional alignment within RIS. Results indicate that regional integration is mediated by the alignment of institutional specialization and moderated by institutional homophily. These findings underscore the critical role of the MUS backbone that supports RIS integration and generates resiliency to socio-economic shocks. Moreover, MUS provide institutional redundancy and variation that generates a broad combinatorial space fostering multi-university research synergies. All together our framework can help address the innovator’s dilemma of whether to exploit institution-specific capabilities or to strategically identify and invest in novel multi-institutional synergies that leverage the complex configurational space of institutional specializations that uniquely characterize each RIS.
the 2022 global monkeypox (Mpox) outbreak revealed sexual transmission as the dominant mode of spread, disproportionately affecting men who have sex with men (MSM) and creating novel challenges in HIV-endemic populations. We present the first mechanistic model capturing bidirectional HIV-Mpox interactions, incorporating three critical innovations: (1) HIV-induced immunological modulation of Mpox progression, (2) antiretroviral therapy (ART)-dependent transmission rates, and (3) vaccination stratification by HIV status. Our analysis demonstrates that HIV co-infection generates a backward bifurcation (critical threshold R c = 0.83 ), enabling Mpox persistence even when R 0 < 1, a phenomenon absent in single-pathogen models. The model reveals that untreated HIV increases Mpox susceptibility by 2.3-fold (95% CI: 2.1-2.6), while current vaccination strategies show 38% reduced efficacy in advanced HIV cases (CD4+ < 200 cells/mm 3 ). Crucially, we identify an optimal intervention window where 60% ART coverage combined with targeted vaccination reduces co-infection prevalence by 5.7-fold (95% CI: 5.2-6.3) compared to isolated approaches. These findings resolve three key gaps in the 2022 response: (i) lack of co-infection-specific transmission metrics, (ii) unquantified ART-vaccination synergies, and (iii) HIV-stratified vaccine efficacy estimates. Our results provide a framework for integrated HIV-Mpox control, demonstrating that coordinated testing and prevention campaigns outperform sequential interventions by 21–34% across epidemiological scenarios.
Component systems - ensembles of realizations built from a shared repertoire of modular parts - are ubiquitous in biological, ecological, technological, and socio-cultural domains. From genomes to texts, cities, and software, these systems exhibit statistical regularities that often meet the "bona fide" requirements of laws in the physical sciences. Here, we argue that the generality and simplicity of those laws are often due to basic combinatorial or sampling constraints, raising the question of whether such patterns are actually revealing system-specific mechanisms and how we might move beyond them. To this end, we first present a unifying mathematical framework, which allows us to compare modular systems in different fields and highlights the common "null" trends as well as the system-specific uniqueness, which, arguably, are signatures of the underlying generative dynamics. Next, we can exploit the framework with statistical mechanics and modern machine-learning tools for a twofold objective. (i) Explaining why the general regularities emerge, highlighting the constraints between them and the general principles at their origins, and (ii) "subtracting" them from data, which will isolate the informative features for inferring hidden system-specific generative processes, mechanistic and causal aspects.
This study focus on the contrasting dynamics of discussion and information dissemination on two influential platforms: Twitter and Wikipedia. While Twitter often serves as a battleground for contentious debates, where users engage in direct confrontation, Wikipedia fosters a collaborative environment aimed at reaching consensus. Focusing on polarizing issues such as the ongoing Russian invasion of Ukraine, the research examines how information is shared, contested, and shaped within these distinct communities. Data from Twitter, collected using the hashtag #UkraineRussiaWar, undergoes a NLP process to categorize and highlight the diverse topics discussed. By classifying the data into Pro-war or Against-war perspectives, we analyze user reactions and the primary themes driving these discussions. For Wikipedia, we gathered and analyzed comments and contributions from various authors, providing insights into the collaborative discourse and consensus-building process.
The ability to detect and characterize online information campaigns will rely on the ability to infer shared underlying narrative viewpoints of online content. Existing methods typically can cluster documents by topic but struggle to distinguish between different points of view. The task is challenging due to the inherent characteristics of social media texts, which are noisy, short, and often provide very little context. Yet, due to the widespread prevalence of harmful misinformation in online media, the development of viewpoint detection approaches is crucial to enable the identification of information campaigns, the characterization of their sources, and their evolution. Our work proposes a weakly supervised contrastive representation learning approach to infuse latent text representations with viewpoint information by leverPlease check and confirm if the authors given and family names have been correctly identified.aging proxy signals observed through social interaction networks. We test our solution on a Twitter dataset related to COVID-19 discussions. We demonstrate the ability of our approach to separate COVID-19 tweets by their narrative viewpoint compared to baseline pre-trained embeddings.
The assortative behavior of a network is the tendency of similar (or dissimilar) nodes to connect to each other. This tendency can have an influence on various properties of the network, such as its robustness or the dynamics of spreading processes. In this paper, we study degree assortativity both in real-world networks and in several generative models for networks with heavy-tailed degree distribution based on latent spaces. In particular, we study Chung-Lu Graphs and Geometric Inhomogeneous Random Graphs (GIRGs). Previous research on assortativity has primarily focused on measuring the degree assortativity in real-world networks using the Pearson assortativity coefficient, despite reservations against this coefficient. We rigorously confirm these reservations by mathematically proving that the Pearson assortativity coefficient does not measure assortativity in any network with sufficiently heavy-tailed degree distributions, which is typical for real-world networks. Moreover, we find that other single-valued assortativity coefficients also do not sufficiently capture the wiring preferences of nodes, which often vary greatly by node degree. We therefore take a more fine-grained approach, analyzing a wide range of conditional and joint weight and degree distributions of connected nodes, both numerically in real-world networks and mathematically in the generative graph models. We provide several methods of visualizing the results. We show that the generative models are assortativity-neutral, while many real-world networks are not. Therefore, we also propose an extension of the GIRG model which retains the manifold desirable properties induced by the degree distribution and the latent space, but also exhibits tunable assortativity. We analyze the resulting model mathematically, and give a fine-grained quantification of its assortativity.
