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Opinion dynamics on tie-decay networks

Physical Review Research 2021年第2期3卷 023249-023249页

作者： Kashin Sugishita Mason A. Porter Mariano Beguerisse-Díaz Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA and Faculty of Science and Engineering Waseda University 169-8555 Tokyo Japan

In social networks, interaction patterns typically change over time. We study opinion dynamics on tie-decay networks in which tie strength increases instantaneously when there is an interaction and decays exponentially between interactions. Specifically, we formulate continuous-time Laplacian dynamics and a discrete-time DeGroot model of opinion dynamics on these tie-decay networks, and we carry out numerical computations for the continuous-time Laplacian dynamics. We examine the speed of convergence by studying the spectral gaps of combinatorial Laplacian matrices of tie-decay networks. First, we compare the spectral gaps of the Laplacian matrices of tie-decay networks that we construct from empirical data with the spectral gaps for corresponding randomized and aggregate networks. We find that the spectral gaps for the empirical networks tend to be smaller than those for the randomized and aggregate networks. Second, we study the spectral gap as a function of the tie-decay rate and time. Intuitively, we expect small tie-decay rates to lead to fast convergence because the influence of each interaction between two nodes lasts longer for smaller decay rates. Moreover, as time progresses and more interactions occur, we expect eventual convergence. However, we demonstrate that the spectral gap need not decrease monotonically with respect to the decay rate or increase monotonically with respect to time. Our results highlight the importance of the interplay between the times that edges strengthen and decay in temporal networks.

关键词： Network structure Social networks Consensus & non-consensus models

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Metapopulation models imply non-Poissonian statistics of interevent times

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Physical Review Research 2022年第1期4卷 013050-013050页

作者： Elohim Fonseca dos Reis Naoki Masuda Department of Mathematics State University of New York at Buffalo Buffalo 14260-2900 New York USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo 14260-5030 New York USA Faculty of Science and Engineering Waseda University Tokyo 169-8555 Japan

Interevent times in temporal contact data from humans and animals typically obey heavy-tailed distributions, which impacts contagion and other dynamical processes on networks. We theoretically show that distributions of interevent times heavier-tailed than exponential distributions are a consequence of the most basic metapopulation model used in epidemiology and ecology, in which individuals move from one patch to another according to the simple random walk. Our results hold true irrespective of the network structure and also for more realistic mobility rules such as high-order random walks and the recurrent mobility patterns used for modeling human dynamics.

关键词： Continuous-time random walk Population dynamics Networks & random structures Social networks Diffusion & random walks Markovian processes Non-Markovian processes

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Dimension reduction of dynamical systems on networks with leading and non-leading eigenvectors of adjacency matrices

arXiv

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arXiv 2022年

作者： Masuda, Naoki Kundu, Prosenjit Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Dimension reduction techniques for dynamical systems on networks are considered to promote our understanding of the original high-dimensional dynamics. One strategy of dimension reduction is to derive a low-dimensional dynamical system whose behavior approximates the observables of the original dynamical system that are weighted linear summations of the state variables at the different nodes. Recently proposed methods use the leading eigenvector of the adjacency matrix of the network as the mixture weights to obtain such observables. In the present study, we explore performances of this type of one-dimensional reductions of dynamical systems on networks when we use non-leading eigenvectors of the adjacency matrix as the mixture weights. Our theory predicts that non-leading eigenvectors can be more efficient than the leading eigenvector and enables us to select the eigenvector minimizing the error. We numerically verify that the optimal non-leading eigenvector outperforms the leading eigenvector for some dynamical systems and networks. We also argue that, despite our theory, it is practically better to use the leading eigenvector as the mixture weights to avoid misplacing the bifurcation point too distantly and to be resistant against dynamical noise. © 2022, CC BY.

关键词： Eigenvalues and eigenfunctions

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Early Warnings for Multistage Transitions in Dynamics on Networks

arXiv

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arXiv 2022年

作者： MacLaren, Neil G. Kundu, Prosenjit Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multistage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multistage transition. We found that knowledge of both the ongoing multistage transition and network structure enables us to calculate effective early warning signals for multistage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multistage transition, which we systematically characterize. © 2022, CC BY.

关键词： Dynamics

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Finite-size effects on the convergence time in continuous-opinion dynamics

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Physical Review E 2021年第1期104卷 014309-014309页

作者： Hang-Hyun Jo Naoki Masuda Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea Department of Mathematics State University of New York at Buffalo Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo Buffalo New York 14260-5030 USA

We study finite-size effects on the convergence time in a continuous-opinion dynamics model. In the model, each individual's opinion is represented by a real number on a finite interval, e.g., [0,1], and a uniformly randomly chosen individual updates its opinion by partially mimicking the opinion of a uniformly randomly chosen neighbor. We numerically find that the characteristic time to the convergence increases as the system size increases according to a particular functional form in the case of lattice networks. In contrast, unless the individuals perfectly copy the opinion of their neighbors in each opinion updating, the convergence time is approximately independent of the system size in the case of regular random graphs, uncorrelated scale-free networks, and complete graphs. We also provide a mean-field analysis of the model to understand the case of the complete graph.

关键词： Complex systems Nonequilibrium statistical mechanics Social systems Homogeneous networks Scale free & inhomogeneous networks Social dynamics Master equation Monte Carlo methods Network Models

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An analysis of reconstruction noise from undersampled 4D flow MRI

arXiv

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arXiv 2022年

作者： Partin, Lauren Schiavazzi, Daniele E. Long, Carlos A. Sing Department Of Applied And Computational Mathematics And Statistics University Of Notre Dame Notre DameIN United States Institute For Mathematical And Computational Engineering Institute For Biological And Medical Engineering Pontificia Universidad Católica De Chile Santiago Chile ANID - Millennium Science Initiative Program Millennium Nucleus Center For The Discovery Of Structures In Complex Data Chile ANID - Millennium Science Initiative Program Millennium Nucleus Center For Cardiovascular Magnetic Resonance Chile

Novel Magnetic Resonance (MR) imaging modalities can quantify hemodynamics but require long acquisition times, precluding its widespread use for early diagnosis of cardiovascular disease. To reduce the acquisition times, reconstruction methods from undersampled measurements are routinely used, that leverage representations designed to increase image compressibility. Reconstructed anatomical and hemodynamic images may present visual artifacts. Although some of these artifact are essentially reconstruction errors, and thus a consequence of undersampling, others may be due to measurement noise or the random choice of the sampled frequencies. Said otherwise, a reconstructed image becomes a random variable, and both its bias and its covariance can lead to visual artifacts;the latter leads to spatial correlations that may be misconstrued for visual information. Although the nature of the former has been studied in the literature, the latter has not received as much attention. In this study, we investigate the theoretical properties of the random perturbations arising from the reconstruction process, and perform a number of numerical experiments on simulated and MR aortic flow. Our results show that the correlation length remains limited to two to three pixels when a Gaussian undersampling pattern is combined with recovery algorithms based on 1-norm minimization. However, the correlation length may increase significantly for other undersampling patterns, higher undersampling factors (i.e., 8× or 16× compression), and different reconstruction methods. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

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Fixation probability in evolutionary dynamics on switching temporal networks

arXiv

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arXiv 2023年

作者： Bhaumik, Jnanajyoti Masuda, Naoki Department of Mathematics State University of New York BuffaloNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program State University of New York at Buffalo BuffaloNY14260-5030 United States

Population structure has been known to substantially affect evolutionary dynamics. Networks that promote the spreading of fitter mutants are called amplifiers of selection, and those that suppress the spreading of fitter mutants are called suppressors of selection. Research in the past two decades has found various families of amplifiers while suppressors still remain somewhat elusive. It has also been discovered that most networks are amplifiers of selection under the birth-death updating combined with uniform initialization, which is a standard condition assumed widely in the literature. In the present study, we extend the birth-death processes to temporal (i.e., time-varying) networks. For the sake of tractability, we restrict ourselves to switching temporal networks, in which the network structure deterministically alternates between two static networks at constant time intervals or stochastically in a Markovian manner. We show that, in a majority of cases, switching networks are less amplifying than both of the two static networks constituting the switching networks. Furthermore, most small switching networks, i.e., networks on six nodes or less, are suppressors, which contrasts to the case of static networks. © 2023, CC BY.

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Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Forecasting Vegetation Health in the MENA Region by Predicti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Sachi Perera Wenzhao Li Erik Linstead Hesham El-Askary Computational and Data Sciences Graduate Program Schmid College of Science and Technology Chapman University Orange CA USA Schmid College of Science and Technology Chapman University Orange CA USA Fowler School of Engineering Chapman University Orange CA USA Faculty of Science Alexandria University Alexandria Egypt

ISBN: (数字)9781728163741

ISBN: (纸本)9781728163758

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R 2 > 0.95) in the testing and (R 2 > 0.93) in the evaluation process.

关键词： Vegetation mapping MODIS Atmospheric modeling Remote sensing Predictive models Training Support vector machines

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APIR:Aggregating Universal Proteomics database Search Algorithms for Peptide Identification with FDR Control

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Genomics, Proteomics & Bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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Advancing the Use of Longitudinal Electronic Health Records: Tutorial for Uncovering Real-World Evidence in Chronic Disease Outcomes

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Journal of medical Internet research 2025年 27卷 e71873页

作者： Feiqing Huang Jue Hou Ningxuan Zhou Kimberly Greco Chenyu Lin Sara Morini Sweet Jun Wen Lechen Shen Nicolas Gonzalez Sinian Zhang Katherine P Liao Tianrun Cai Zongqi Xia Florence T Bourgeois Tianxi Cai Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA United States. Harvard-MIT Center for Regulatory Science Harvard Medical School Boston MA United States. Division of Biostatistics and Health Data Science School of Public Health University of Minnesota Minneapolis MN United States. Department of Biomedical Informatics Harvard Medical School Boston MA United States. Department of Engineering University of Toronto Toronto ON Canada. Division of Rheumatology Inflammation and Immunity Brigham and Women's Hospital Boston MA United States. Department of Neurology University of Pittsburgh Pittsburgh PA United States. Computational Health Informatics Program (CHIP) Boston Children's Hospital Boston MA United States. Department of Pediatrics Harvard Medical School Boston MA United States.

Managing chronic diseases requires ongoing monitoring of disease activity and therapeutic responses to optimize treatment plans. With the growing availability of disease-modifying therapies, it is crucial to investigate comparative effectiveness and long-term outcomes beyond those available from randomized clinical trials. We introduce a comprehensive pipeline for generating reproducible and generalizable real-world evidence on disease outcomes by leveraging electronic health record data. The pipeline first generates scalable disease outcomes by linking electronic health record data with registry data containing a small sample of labeled outcomes. It then applies causal analysis using these scalable outcomes to evaluate therapies for chronic diseases. The implementation of the pipeline is illustrated in a case study based on multiple sclerosis. Our approach addresses challenges in real-world evidence generation for disease activity of chronic conditions, specifically the lack of direct observations on key outcomes and biases arising from imperfect or incomplete data. We present advanced machine learning techniques such as semisupervised and ensemble methods to impute missing outcome data, further incorporating steps for calibrated causal analyses and bias correction.

关键词： calibration causal inference chronic disease outcomes data imputation electronic health records longitudinal disease activity machine learning real-world evidence

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