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版本更新 · v3arXiv:2604.22791

R Package iglm: Regression under Interference in Connected Populations

Cornelius Fritz, Michael Schweinberger

原文摘要

We introduce R package iglm, which implements a unifying regression framework for studying relationships among binary, count-, and real-valued predictors and outcomes in connected populations with interference due to spillover, contagion, and other complex phenomena. On the computational side, the regression framework provides scalable methods that can be applied to small and large data sets, by solving a convex optimization program based on pseudo-likelihoods via accelerated Minorization-Maximization algorithms. On the statistical side, the regression framework comes with provable theoretical guarantees for statistical estimators, including consistency results and rates of convergence for maximum pseudo-likelihood estimators. To increase the versatility of iglm, users can add custom-built model terms. We showcase iglm using data on hate speech on the social media platform X and communications among students.

stat.CO · cs.SI · stat.OT

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.14499

尼泰罗伊交通选择的空间格局

Spatial patterns of transportation choice in Niterói

Sandro Martinelli Reia, Taylor Anderson, Dieter Pfoser

AI 速读 基于摘要

作者提出一个利用宏观聚合静态城市数据构建微观动态出行模拟的计算框架，并在巴西尼泰罗伊约47.5万合成居民上复现邻里尺度交通方式选择的空间差异。

physics.soc-ph · cs.MA · stat.OT

arXiv ↗PDF ↗
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Characterizing how people move through a city requires spatially and temporally fine-grained data that are often costly and difficult to obtain. Here, we introduce a computational framework that uses macroscopic, aggregated, and static urban data to construct a microscopic, dynamic, and spatially detailed representation of urban mobility. Building on an existing activity simulator, the framework combines a synthetic population, the built environment, and transport networks to generate daily trips and assign walking, car, or public transit. Applied to approximately 475,000 synthetic residents of Niterói, Brazil, simulated neighborhood-level transit patterns show a strong association with an empirical activity proxy (Pearson r=0.885). Walking is more prevalent in compact central areas, car use increases toward the periphery, and public-transit use remains comparatively high along bus corridors. Reducing walking times to and from bus stops by 30% increases the modeled public-transit-to-car ratio by 11.3%, with larger transit-share gains in several peripheral areas. These results reveal spatial differences in transportation choice and identify where improved transit access may have the greatest modeled effect.

新论文 · v1arXiv:2611.11535

Addressing Bias Early in the AI Lifecycle: Social Determinants of Health in Functional and Cognitive Assessment Labels of Nursing Home Residents

Isabel Amaya-Rodríguez, Cristina Martin, Nekane Larburu, Teresa Garcia-Navarro, Roberto González Novas, Alberto Rodríguez Taboada, Maite García-Sebastian, Ainara Estanga 等 13 位作者

原文摘要

Fairness in clinical artificial intelligence (AI) is usually evaluated after model training, yet bias can enter earlier in the AI lifecycle, during data collection and labelling. When routine clinical assessments serve as target variables, systematic differences in these labels across sociodemographic groups are carried into model development, validation and updating. This study addresses this early lifecycle stage by auditing labels from three standardised instruments used in residential care: the Barthel Index (BI), the Mini-Mental State Examination (MMSE) and the Global Deterioration Scale (GDS). Using electronic health records of 2,407 residents from 12 nursing homes in the Basque Country, Spain (2011-2021), we assessed four social determinants of health (SDoH) - sex, educational attainment, lifetime work activity and marital status - with two group fairness metrics, Disparate Impact (DI) and Statistical Parity Difference (SPD), computed for each outcome category with bootstrap confidence intervals. Disparities were found for all four attributes and across the three instruments: men, residents with intermediate education and never-partnered residents were more frequently classified in categories indicating preserved function or cognition. These disparities may reflect genuine differences in health status, measurement properties of the instruments, assessment practices or selective admission to residential care, and cannot be attributed to a single cause with the present design. Regardless of their origin, any model trained on these labels would inherit them, and they could persist through later lifecycle stages unless explicitly examined. These findings support pre-training label auditing as the first step of a continuous fairness assessment spanning model development, validation, deployment and updating.

cs.LO · stat.OT

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arXiv ↗PDF ↗
版本更新 · v5arXiv:2504.20941

Conformal-DP: A Density-Aware Mechanism for Differential Privacy over Riemannian Manifolds via Conformal Transformation

Peilin He, Liou Tang, M. Amin Rahimian, James Joshi

原文摘要

Differential Privacy (DP) is being increasingly adopted for non-Euclidean data that lie on complex, high-dimensional manifolds. Existing DP mechanisms for manifold data consider geometric properties when calibrating privacy perturbations, but they largely fail to capture variations in data density within datasets, leading to biased perturbations and suboptimal privacy-utility trade-offs due to heterogeneous data distributions. In this paper, we propose a novel density-aware differential privacy mechanism on Riemannian manifolds, referred to as Conformal-DP, that leverages conformal transformations to calibrate perturbations based on local densities and to induce a density-balanced geometry. We prove that our mechanism satisfies $ε$-differential privacy on any complete Riemannian manifold under mild regularity assumptions. In addition, we derive a closed-form expected geodesic error bound that depends only on the underlying data density ratio and is independent of global curvature. Our empirical results on synthetic and real-world datasets demonstrate that the proposed Conformal-DP mechanism substantially improves the privacy-utility trade-off in heterogeneous data distribution settings, with worst-case performance comparable to state-of-the-art manifold DP mechanisms that assume uniformly distributed data.

cs.CR · math.DG · stat.OT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.30905

Network Analysis in Communication Research: A Review of Methods, Theories, and Research Practices

Pengjia Cui

原文摘要

Network analysis has become a standard way of representing consultation, sourcing, media use, attention, and influence in communication research. This review examines 31 applications across communication subfields and asks of each what communication problem it addresses, which explanatory resource it invokes, and what its research practice makes observable. The three dimensions form a comparison framework and yield three propositions with stated scope and refutation conditions: resource relevance is task- and criterion-dependent; commonality at one level of aggregation does not imply commonality at another; and influence estimates are conditional on relationship type, group stage, and time. A short bibliometric section describes how the literature's cited references are organised: method references are paired with one another more often than year-specific margins predict (ratio 1.36) and with theoretical references less often (0.82), a pattern consistent with a methodological toolkit that circulates apart from the commitments that license its use. The review closes with designs that would test each proposition.

cs.SI · stat.OT

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arXiv ↗PDF ↗