arXiv subjects

Jukka-Pekka Onnela

Publications and source records attributed to Jukka-Pekka Onnela.

At least 19 recordsLinked to original sources

arXiv2026

CCMnet: A Software Package for Network Generation with Congruence Class Models

We introduce CCMnet, an R package designed to generate network ensembles that accurately reflect the uncertainty inherent in empirical data. While traditional network modeling often results in ensembles with fixed property values or model-determined levels of variability, CCMnet enables a continuous spectrum of variability for network properties, including edge counts, degree distribution, and mixing patterns. By defining probability distributions directly over congruence classes of networks, the package allows researchers to specify the uncertainty in network properties across the generated ensemble to match a specific sampling design or empirical distribution. Furthermore, this formulation provides a principled framework that encompasses several classic models (e.g., Erdős--Rényi model, stochastic block models, and certain exponential random graph models) that implicitly share this structural basis, while offering the flexibility to specify arbitrary, even non-parametric, distributions for network properties. CCMnet implements a Markov chain Monte Carlo (MCMC) framework to sample from these models. The utility of the package is illustrated by generating posterior predictive network ensembles representing school friendship networks.

stat.CO↗

arXiv2026

Covariate Selection for Joint Latent Space Modeling of Sparse Network Data

Network data are increasingly common in the social sciences and infectious disease epidemiology. Analyses often link network structure to node-level covariates, but existing methods falter with sparse networks and high-dimensional node features. We propose a joint latent space modeling framework for sparse networks with high-dimensional binary node covariates that performs covariate selection while accounting for uncertainty in estimated latent positions. Building on joint latent space models that couple edges and node variables through shared latent positions, we introduce a group lasso screening step and incorporate a measurement-error-aware stabilization term to mitigate bias from using estimated latent positions as predictors. We establish prediction error rates for the covariate component both when latent positions are treated as observed and when they are estimated with bounded error; under uniform control across $q$ covariates and $n$ nodes, the rate is of order $O(\log q / n)$ up to an additional term due to latent position estimation error. Our method addresses three challenges: (1) incorporating information from isolated nodes, which are common in sparse networks but often ignored; (2) selecting relevant covariates from high-dimensional spaces; and (3) accounting for uncertainty in estimated latent positions. Simulations show predictive performance remains stable as covariate sparsity grows, while naive approaches degrade. We illustrate how the method can support efficient study design using household social networks from 75 Indian villages, where an emulated pilot study screens a large covariate battery and substantially reduces required subsequent data collection without sacrificing network predictive accuracy.

stat.ME↗

arXiv2025

Information is localized in growing network models

Mechanistic network models can capture salient characteristics of empirical networks using a small set of domain-specific, interpretable mechanisms. Yet inference remains challenging because the likelihood is often intractable. We show that, for a broad class of growing network models, information about model parameters is localized in the network, i.e., the likelihood can be expressed in terms of small subgraphs. We take a Bayesian perspective to inference and develop neural density estimators (NDEs) to approximate the posterior distribution of model parameters using graph neural networks (GNNs) with limited receptive size, i.e., the GNN can only "see" small subgraphs. We characterize nine growing network models in terms of their localization and demonstrate that localization predictions agree with NDEs on simulated data. Even for non-localized models, NDEs can infer high-fidelity posteriors matching model-specific inference methods at a fraction of the cost. Our findings establish information localization as a fundamental property of network growth, theoretically justifying the analysis of local subgraphs embedded in larger, unobserved networks and the use of GNNs with limited receptive field for likelihood-free inference.

cs.SI↗

arXiv2025

Approximate Bayesian Inference on Mechanisms of Network Growth and Evolution

Mechanistic models can provide an intuitive and interpretable explanation of network growth by specifying a set of generative rules. These rules can be defined by domain knowledge about real-world mechanisms governing network growth or may be designed to facilitate the appearance of certain network motifs. In the formation of real-world networks, multiple mechanisms may be simultaneously involved; it is then important to understand the relative contribution of each of these mechanisms. In this paper, we propose the use of a conditional density estimator, augmented with a graph neural network, to perform inference on a flexible mixture of network-forming mechanisms. This event-wise mixture-of-mechanisms model assigns mechanisms to each edge formation event rather than stipulating node-level mechanisms, thus allowing for an explanation of the network generation process, as well as the dynamic evolution of the network over time. We demonstrate that our approximate Bayesian approach yields valid inferences for the relative weights of the mechanisms in our model, and we utilize this method to investigate the mechanisms behind the formation of a variety of real-world networks.

cs.SI↗

arXiv2025

Unifying Summary Statistic Selection for Approximate Bayesian Computation

Extracting low-dimensional summary statistics from large datasets is essential for efficient (likelihood-free) inference. We characterize three different classes of summaries and demonstrate their importance for correctly analyzing dimensionality reduction algorithms. We demonstrate that minimizing the expected posterior entropy (EPE) under the prior predictive distribution of the model provides a unifying principle that subsumes many existing methods; they are shown to be equivalent to, or special or limiting cases of, minimizing the EPE. We offer a unifying framework for obtaining informative summaries and propose a practical method using conditional density estimation to learn high-fidelity summaries automatically. We evaluate this approach on diverse problems, including a challenging benchmark model with a multi-modal posterior, a population genetics model, and a dynamic network model of growing trees. The results show that EPE-minimizing summaries can lead to posterior inference that is competitive with, and in some cases superior to, dedicated likelihood-based approaches, providing a powerful and general tool for practitioners.

stat.ME↗

arXiv2025

Describe Where You Are: Improving Noise-Robustness for Speech Emotion Recognition with Text Description of the Environment

Speech emotion recognition (SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER performance under noisy conditions. To address this task, we propose a text-guided, environment-aware training where an SER model is trained with contaminated speech samples and their paired noise description. We use a pre-trained text encoder to extract the text-based environment embedding and then fuse it to a transformer-based SER model during training and inference. We demonstrate the effectiveness of our approach through our experiment with the MSP-Podcast corpus and real-world additive noise samples collected from the Freesound and DEMAND repositories. Our experiment indicates that the text-based environment descriptions processed by a large language model (LLM) produce representations that improve the noise-robustness of the SER system. With a contrastive learning (CL)-based representation, our proposed method can be improved by jointly fine-tuning the text encoder with the emotion recognition model. Under the -5dB signal-to-noise ratio (SNR) level, fine-tuning the text encoder improves our CL-based representation method by 76.4% (arousal), 100.0% (dominance), and 27.7% (valence).

cs.SD↗

arXiv2025

Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection

We introduce a novel \textit{k}-nearest neighbor (\textit{k}-NN) regression method for joint estimation of the conditional mean and variance. The proposed algorithm preserves the computational efficiency and manifold-learning capabilities of classical non-parametric \textit{k}-NN models, while integrating a data-driven variable selection step that improves empirical performance. By accurately estimating both conditional mean and variance regression functions, the method effectively reconstructs the conditional distribution and density functions for multiple families of scale-and-localization generative models. We show that our estimator can achieve fast convergence rates, and we derive practical rules for selecting the smoothing parameter~$k$ that enhance the precision of the algorithm in finite sample regimes. Extensive simulations for low, moderate and large-dimensional covariate spaces, together with a real-world biomedical application, demonstrate that the proposed method can consistently outperform the conventional \textit{k-NN} regression algorithm while being more interpretable in the model output.

stat.ME↗

arXiv2025

Identification and Estimation of Heterogeneous Interference Effects under Unknown Network

Interference--in which a unit's outcome is affected by the treatment of other units--poses significant challenges for the identification and estimation of causal effects. Most existing methods for estimating interference effects assume that the interference networks are known. In many practical settings, this assumption is unrealistic as such networks are typically latent. To address this challenge, we propose a novel framework for identifying and estimating heterogeneous group-level interference effects without requiring a known interference network. Specifically, we assume a shared latent community structure between the observed network and the unknown interference network. We demonstrate that interference effects are identifiable if and only if group-level interference effects are heterogeneous, and we establish the consistency and asymptotic normality of the maximum likelihood estimator (MLE). To handle the intractable likelihood function and facilitate the computation, we propose a Bayesian implementation and show that the posterior concentrates around the MLE. A series of simulation studies demonstrate the effectiveness of the proposed method and its superior performance compared with competitors. We apply our proposed framework to the encounter data of stroke patients from the California Department of Healthcare Access and Information (HCAI) and evaluate the causal interference effects of certain intervention in one hospital on the outcomes of other hospitals.

stat.ME↗

arXiv2025

Community Detection through Recursive Partitioning in Bayesian Framework

Community detection involves grouping the nodes in the network and is one of the most-studied tasks in network science. Conventional methods usually require the specification of the number of communities $K$ in the network. This number is determined heuristically or by certain model selection criteria. In practice, different model selection criteria yield different values of $K$, leading to different results. We propose a community detection method based on recursive partitioning within the Bayesian framework. The method is compatible with a wide range of existing model-based community detection frameworks. In particular, our method does not require pre-specification of the number of communities and can capture the hierarchical structure of the network. We establish the theoretical guarantee of consistency under the stochastic block model and demonstrate the effectiveness of our method through simulations using different models that cover a broad range of scenarios. We apply our method to the California Department of Healthcare Access and Information (HCAI) data, including all Emergency Department (ED) and hospital discharges from 342 hospitals to identify regional hospital clusters.

stat.ME↗

arXiv2025

Bayesian Inference for Sexual Contact Networks Using Longitudinal Survey Data

Characterizing sexual contact networks is essential for understanding sexually transmitted infections, but principled parameter inference for mechanistic network models remains challenging. We develop a discrete-time simulation framework that enables parameter estimation using approximate Bayesian computation. The interpretable model incorporates relationship formation, dissolution, concurrency, casual contacts, and population turnover. Applying our framework to survey data from 403 men who have sex with men in Stockholm, we provide principled uncertainty quantification for key network dynamics. Our analysis estimates the timescale for seeking a new steady relationship at 25 weeks and for relationship dissolution at 42 weeks. Casual contacts occur more frequently for single individuals (every 1.8 weeks) than for partnered individuals (every 4.5 weeks). However, while cross-sectional data constrains these parameters, migration rates remain poorly identified. We demonstrate that simple longitudinal data can resolve this issue. Tracking participant retention between survey waves directly informs migration rates, though survey dropout is a potential confounder. Furthermore, simple binary survey questions can outperform complex timeline follow-back methods for estimating contact frequencies. This framework provides a foundation for uncertainty quantification in network epidemiology and offers practical strategies to improve inference from surveys, the primary data source for studying sexual behavior.

stat.ME↗

arXiv2025

SkipTrack: A Bayesian Hierarchical Model for Self-tracked Menstrual Cycle Length and Regularity in Large Mobile Health Cohorts

Menstrual cycle length and regularity are important vital signs with implications for a variety of acute and chronic health conditions. Large datasets derived from cycle-tracking mobile health apps are being used to investigate the effects of various covariates on menstrual cycle length and regularity. One limitation on these analyses is that recorded cycle lengths can be incorrectly inflated if users skip tracking any cycle related bleeding days in the app. Here we present SkipTrack, a novel Bayesian hierarchical framework for examining baseline and time-varying effects on menstrual cycle length and regularity while accounting for the uncertainty of possible skips in cycle tracking. In simulations we demonstrate the superiority of the SkipTrack model by showing that competing methods which specify cycle skips a priori are more susceptible to issues of estimation bias and overconfidence than the SkipTrack model. Finally, we apply the SkipTrack framework to data from the Apple Women's Healthy Study, a US-based digital cohort (consent provided at study enrollment) to examine patterns of association between age, BMI and race/ethnicity, and menstrual cycle length and regularity.

stat.AP↗

arXiv2025

Missing data in non-stationary multivariate time series from digital studies in Psychiatry

Mobile technology (e.g., mobile phones and wearable devices) provides scalable methods for collecting physiological and behavioral biomarkers in patients' naturalistic settings, as well as opportunities for therapeutic advancements and scientific discoveries regarding the etiology of psychiatric illness. Continuous data collection through mobile devices generates highly complex data: entangled multivariate time series of outcomes, exposures, and covariates. Missing data is a pervasive problem in biomedical and social science research, and Ecological Momentary Assessment (EMA) data in psychiatric research is no exception. However, the complex data structure of multivariate time series and their non-stationary nature make missing data a major challenge for proper inference. Additional historical information included in time series analyses exacerbates the issue of missing data and also introduces problems for confounding adjustment. The majority of existing imputation methods are either designed for stationary time series or for longitudinal data with limited follow-up periods. The limited work on non-stationary time series either focuses on missing exogenous information or ignores the complex temporal dependence among outcomes, exposures, and covariates. We propose a Monte Carlo Expectation Maximization algorithm for the state space model (MCEM-SSM) to effectively handle missing data in non-stationary entangled multivariate time series. We demonstrate the method's advantages over other widely used missing data imputation strategies through simulations of both stationary and non-stationary time series, subject to various missing mechanisms. Finally, we apply the MCEM-SSM to a multi-year smartphone observational study of bipolar and schizophrenia patients to investigate the association between digital social connectivity and negative mood.

stat.ME↗

arXiv2025

Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces

Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and random graphs. Despite their favorable theoretical properties, the integration of depth measures into regression modeling to provide prediction regions remains a largely underexplored area of research. To address this gap, we propose a novel, model-free uncertainty quantification algorithm based on conditional depth measures--specifically, conditional kernel mean embeddings and an integrated depth measure. These new algorithms can be used to define prediction and tolerance regions when predictors and responses are defined in separable Hilbert spaces. The use of kernel mean embeddings ensures faster convergence rates in prediction region estimation. To enhance the practical utility of the algorithms with finite samples, we also introduce a conformal prediction variant that provides marginal, non-asymptotic guarantees for the derived prediction regions. Additionally, we establish both conditional and unconditional consistency results, as well as fast convergence rates in certain homoscedastic settings. We evaluate the finite--sample performance of our model in extensive simulation studies involving various types of functional data and traditional Euclidean scenarios. Finally, we demonstrate the practical relevance of our approach through a digital health application related to physical activity, aiming to provide personalized recommendations

stat.ML↗

arXiv2025

Nonparametric Additive Value Functions: Interpretable Reinforcement Learning with an Application to Surgical Recovery

We propose a nonparametric additive model for estimating interpretable value functions in reinforcement learning, with an application in optimizing postoperative recovery through personalized, adaptive recommendations. While reinforcement learning has achieved significant success in various domains, recent methods often rely on black-box approaches such as neural networks, which hinder the examination of individual feature contributions to a decision-making policy. Our novel method offers a flexible technique for estimating action-value functions without explicit parametric assumptions, overcoming the limitations of the linearity assumption of classical algorithms. By incorporating local kernel regression and basis expansion, we obtain a sparse, additive representation of the action-value function, enabling local approximation and retrieval of nonlinear, independent contributions of select state features and the interactions between joint feature pairs. We validate our approach through a simulation study and apply it to spine disease recovery, uncovering recommendations aligned with clinical knowledge. This method bridges the gap between flexible machine learning techniques and the interpretability required in healthcare applications, paving the way for more personalized interventions.

stat.ML↗

arXiv2025

Modeling the 2022 Mpox Outbreak with a Mechanistic Network Model

We implemented a dynamic agent-based network model to simulate the spread of mpox in a United States-based MSM population. This model allowed us to implement data-informed dynamic network evolution to simulate realistic disease spreading and behavioral adaptations. We found that behavior change, the reduction in one-time partnerships, and widespread vaccination are effective in preventing the transmission of mpox and that earlier intervention has a greater effect, even when only a high-risk portion of the population participates. With no intervention, 16% of the population was infected (25th percentile, 75th percentiles of simulations: 15.3%, 16.6%). With vaccination and behavior change in only the 25% of individuals most likely to have a one-time partner, cumulative infections were reduced by 30%, or a total reduction in nearly 500 infections. Earlier intervention further reduces cumulative infections; beginning vaccination a year before the outbreak results in only 5.5% of men being infected, averting 950 infections or nearly 10% of the total population in our model. We also show that sustained partnerships drive the early outbreak, while one-time partnerships drive transmission after the first initial weeks. The median effective reproductive number, Rt, at t = 0 days is 1.30 for casual partnerships, 1.00 for main, and 0.6 for one-time. By t = 28, the median Rt for one-time partnerships has more than doubled to 1.48, while it decreased for casual and main partnerships: 0.46 and 0.29, respectively. With the ability to model individuals' behavior, mechanistic networks are particularly well suited to studying sexually transmitted infections, the spread and control of which are often governed by individual-level action. Our results contribute valuable insights into the role of different interventions and relationship types in mpox transmission dynamics.

stat.AP↗

arXiv2025

Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs

Complex survey designs are commonly employed in many medical cohorts. In such scenarios, developing case-specific predictive risk score models that reflect the unique characteristics of the study design is essential for minimizing selective biases in the statistical results. The objectives of this paper are to: (i) propose a general predictive framework for regression and classification using neural network (NN) modeling that incorporates survey weights into the estimation process; (ii) introduce an uncertainty quantification algorithm for model prediction tailored to data from complex survey designs; and (iii) apply this method to develop robust risk score models for assessing the risk of Diabetes Mellitus in the US population, utilizing data from the NHANES 2011-2014 cohort. The results indicate that models of varying complexity, each utilizing a different set of variables, demonstrate different discriminative power for predicting diabetes (with different economic cost), yet yield generalizable results at the population level. Although the focus is on diabetes, this NN predictive framework is adaptable for developing clinical models across a diverse range of diseases and medical cohorts. The software and data used in this paper are publicly available on GitHub.

stat.ME↗

arXiv2025

Individual causal effect estimation accounting for latent disease state modification among bipolar participants in mobile health studies

Individuals with bipolar disorder tend to cycle through disease states such as depression and mania. The heterogeneous nature of disease across states complicates the evaluation of interventions for bipolar disorder patients, as varied interventional success is observed within and across individuals. In fact, we hypothesize that disease state acts as an effect modifier for the causal effect of a given intervention on health outcomes. To address this dilemma, we propose an N-of-1 approach using an adapted autoregressive hidden Markov model, applied to longitudinal mobile health data collected from individuals with bipolar disorder. This method allows us to identify a latent variable from mobile health data to be treated as an effect modifier between the exposure and outcome of interest while allowing for missing data in the outcome. A counterfactual approach is employed for causal inference and to obtain a g-formula estimator to recover said effect. The performance of the proposed method is compared with a naive approach across extensive simulations and application to a multi-year smartphone study of bipolar patients, evaluating the individual effect of digital social activity on sleep duration across different latent disease states.

stat.AP↗

arXiv2024

Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics

Continuous glucose monitoring (CGM) data has revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as hypoglycemia. Recently, there has been growing interest in utilizing CGM devices in clinical studies involving healthy and diabetes populations. However, efficiently exploiting the high temporal resolution of CGM profiles remains a significant challenge. Numerous indices -- such as time-in-range metrics and glucose variability measures -- have been proposed, but evidence suggests these metrics overlook critical aspects of glucose dynamic homeostasis. As an alternative method, this paper explores the clinical value of glucodensity metrics in capturing glucose dynamics -- specifically the speed and acceleration of CGM time series -- as new biomarkers for predicting long-term glucose outcomes. Our results demonstrate significant information gains, exceeding 20\% in terms of adjusted $R^2$, in forecasting glycosylated hemoglobin (HbA1c) and fasting plasma glucose (FPG) at five and eight years from baseline AEGIS data, compared to traditional non-CGM and CGM glucose biomarkers. These findings underscore the importance of incorporating more complex CGM functional metrics, such as the glucodensity approach, to fully capture continuous glucose fluctuations across different time-scale resolutions.

stat.AP↗