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  1. Book ; Online: A Perturbative Solution to the Linear Influence/Network Autocorrelation Model Under Network Dynamics

    Butts, Carter T.

    2023  

    Abstract: Known by many names and arising in many settings, the forced linear diffusion model is central to the modeling of power and influence within social networks (while also serving as the mechanistic justification for the widely used spatial/network ... ...

    Abstract Known by many names and arising in many settings, the forced linear diffusion model is central to the modeling of power and influence within social networks (while also serving as the mechanistic justification for the widely used spatial/network autocorrelation models). The standard equilibrium solution to the diffusion model depends on strict timescale separation between network dynamics and attribute dynamics, such that the diffusion network can be considered fixed with respect to the diffusion process. Here, we consider a relaxation of this assumption, in which the network changes only slowly relative to the diffusion dynamics. In this case, we show that one can obtain a perturbative solution to the diffusion model, which depends on knowledge of past states in only a minimal way.
    Keywords Computer Science - Social and Information Networks ; Mathematics - Statistics Theory
    Publishing date 2023-10-31
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Book ; Online: Continuous Time Graph Processes with Known ERGM Equilibria

    Butts, Carter T.

    Contextual Review, Extensions, and Synthesis

    2022  

    Abstract: Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such ... ...

    Abstract Graph processes that unfold in continuous time are of obvious theoretical and practical interest. Particularly useful are those whose long-term behavior converges to a graph distribution of known form. Here, we review some of the conditions for such convergence, and provide examples of novel and/or known processes that do so. These include subfamilies of the well-known stochastic actor oriented models, as well as continuum extensions of temporal and separable temporal exponential family random graph models. We also comment on some related threads in the broader work on network dynamics, which provide additional context for the continuous time case.

    Comment: Final accepted version
    Keywords Statistics - Methodology ; Computer Science - Discrete Mathematics ; Computer Science - Social and Information Networks ; Mathematics - Statistics Theory
    Publishing date 2022-03-14
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Highly scalable maximum likelihood and conjugate Bayesian inference for ERGMs on graph sets with equivalent vertices.

    Yin, Fan / Butts, Carter T

    PloS one

    2022  Volume 17, Issue 8, Page(s) e0273039

    Abstract: The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, ... ...

    Abstract The exponential family random graph modeling (ERGM) framework provides a highly flexible approach for the statistical analysis of networks (i.e., graphs). As ERGMs with dyadic dependence involve normalizing factors that are extremely costly to compute, practical strategies for ERGMs inference generally employ a variety of approximations or other workarounds. Markov Chain Monte Carlo maximum likelihood (MCMC MLE) provides a powerful tool to approximate the maximum likelihood estimator (MLE) of ERGM parameters, and is generally feasible for typical models on single networks with as many as a few thousand nodes. MCMC-based algorithms for Bayesian analysis are more expensive, and high-quality answers are challenging to obtain on large graphs. For both strategies, extension to the pooled case-in which we observe multiple networks from a common generative process-adds further computational cost, with both time and memory scaling linearly in the number of graphs. This becomes prohibitive for large networks, or cases in which large numbers of graph observations are available. Here, we exploit some basic properties of the discrete exponential families to develop an approach for ERGM inference in the pooled case that (where applicable) allows an arbitrarily large number of graph observations to be fit at no additional computational cost beyond preprocessing the data itself. Moreover, a variant of our approach can also be used to perform Bayesian inference under conjugate priors, again with no additional computational cost in the estimation phase. The latter can be employed either for single graph observations, or for observations from graph sets. As we show, the conjugate prior is easily specified, and is well-suited to applications such as regularization. Simulation studies show that the pooled method leads to estimates with good frequentist properties, and posterior estimates under the conjugate prior are well-behaved. We demonstrate the usefulness of our approach with applications to pooled analysis of brain functional connectivity networks and to replicated x-ray crystal structures of hen egg-white lysozyme.
    MeSH term(s) Algorithms ; Bayes Theorem ; Computer Simulation ; Markov Chains ; Monte Carlo Method
    Language English
    Publishing date 2022-08-26
    Publishing country United States
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, N.I.H., Extramural
    ZDB-ID 2267670-3
    ISSN 1932-6203 ; 1932-6203
    ISSN (online) 1932-6203
    ISSN 1932-6203
    DOI 10.1371/journal.pone.0273039
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: California Exodus? A Network Model of Population Redistribution in the United States

    Huang, Peng / Butts, Carter T.

    2023  

    Abstract: Motivated by debates about California's net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow networks in the United States. We introduce a protocol that visualizes the complex effects of ... ...

    Abstract Motivated by debates about California's net migration loss, we employ valued exponential-family random graph models to analyze the inter-county migration flow networks in the United States. We introduce a protocol that visualizes the complex effects of potential underlying mechanisms, and perform in silico knockout experiments to quantify their contribution to the California Exodus. We find that racial dynamics contribute to the California Exodus, urbanization ameliorates it, and political climate and housing costs have little impact. Moreover, the severity of the California Exodus depends on how one measures it, and California is not the state with the most substantial population loss. The paper demonstrates how generative statistical models can provide mechanistic insights beyond simple hypothesis-testing.
    Keywords Computer Science - Social and Information Networks ; Statistics - Applications
    Publishing date 2023-08-12
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: Bayesian estimation of the hydroxyl radical diffusion coefficient at low temperature and high pressure from atomistic molecular dynamics.

    Butts, Carter T / Martin, Rachel W

    The Journal of chemical physics

    2021  Volume 155, Issue 19, Page(s) 194504

    Abstract: The hydroxyl radical is the primary reactive oxygen species produced by the radiolysis of water and is a significant source of radiation damage to living organisms. Mobility of the hydroxyl radical at low temperatures and/or high pressures is hence a ... ...

    Abstract The hydroxyl radical is the primary reactive oxygen species produced by the radiolysis of water and is a significant source of radiation damage to living organisms. Mobility of the hydroxyl radical at low temperatures and/or high pressures is hence a potentially important factor in determining the challenges facing psychrophilic and/or barophilic organisms in high-radiation environments (e.g., ice-interface or undersea environments in which radiative heating is a potential heat and energy source). Here, we estimate the diffusion coefficient for the hydroxyl radical in aqueous solution using a hierarchical Bayesian model based on atomistic molecular dynamics trajectories in TIP4P/2005 water over a range of temperatures and pressures.
    Language English
    Publishing date 2021-10-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 3113-6
    ISSN 1089-7690 ; 0021-9606
    ISSN (online) 1089-7690
    ISSN 0021-9606
    DOI 10.1063/5.0064995
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  6. Book ; Online: Phase Transitions in the Edge/Concurrent Vertex Model

    Butts, Carter T.

    2020  

    Abstract: Although it is well-known that some exponential family random graph model (ERGM) families exhibit phase transitions (in which small parameter changes lead to qualitative changes in graph structure), the behavior of other models is still poorly understood. ...

    Abstract Although it is well-known that some exponential family random graph model (ERGM) families exhibit phase transitions (in which small parameter changes lead to qualitative changes in graph structure), the behavior of other models is still poorly understood. Recently, Krivitsky and Morris have reported a previously unobserved phase transition in the edge/concurrent vertex family (a simple starting point for models of sexual contact networks). Here, we examine this phase transition, showing it to be a first order transition with respect to an order parameter associated with the fraction of concurrent vertices. This transition stems from weak cooperativity in the recruitment of vertices to the concurrent phase, which may not be a desirable property in some applications.
    Keywords Computer Science - Social and Information Networks ; Condensed Matter - Statistical Mechanics ; Mathematics - Statistics Theory
    Publishing date 2020-01-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features.

    Zong, Zixiao / Yang, Mengwei / Ley, Justin / Butts, Carter T / Markopoulou, Athina

    Proceedings on Privacy Enhancing Technologies. Privacy Enhancing Technologies Symposium

    2024  Volume 2023, Issue 1, Page(s) 309–324

    Abstract: We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a ... ...

    Abstract We consider the problem of population density estimation based on location data crowdsourced from mobile devices, using kernel density estimation (KDE). In a conventional, centralized setting, KDE requires mobile users to upload their location data to a server, thus raising privacy concerns. Here, we propose a Federated KDE framework for estimating the user population density, which not only keeps location data on the devices but also provides probabilistic privacy guarantees against a
    Language English
    Publishing date 2024-01-08
    Publishing country Poland
    Document type Journal Article
    ISSN 2299-0984
    ISSN (online) 2299-0984
    DOI 10.56553/popets-2023-0019
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: Network Hamiltonian Models for Unstructured Protein Aggregates, with Application to γD-Crystallin.

    Diessner, Elizabeth M / Freites, J Alfredo / Tobias, Douglas J / Butts, Carter T

    The journal of physical chemistry. B

    2023  Volume 127, Issue 3, Page(s) 685–697

    Abstract: Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, ... ...

    Abstract Network Hamiltonian models (NHMs) are a framework for topological coarse-graining of protein-protein interactions, in which each node corresponds to a protein, and edges are drawn between nodes representing proteins that are noncovalently bound. Here, this framework is applied to aggregates of γD-crystallin, a structural protein of the eye lens implicated in cataract disease. The NHMs in this study are generated from atomistic simulations of equilibrium distributions of wild-type and the cataract-causing variant W42R in solution, performed by Wong, E. K.; Prytkova, V.; Freites, J. A.; Butts, C. T.; Tobias, D. J. Molecular Mechanism of Aggregation of the Cataract-Related γD-Crystallin W42R Variant from Multiscale Atomistic Simulations.
    MeSH term(s) Humans ; Intrinsically Disordered Proteins/metabolism ; Protein Aggregates ; gamma-Crystallins/chemistry ; Cataract/metabolism ; Lens, Crystalline/metabolism
    Chemical Substances Intrinsically Disordered Proteins ; Protein Aggregates ; gamma-Crystallins
    Language English
    Publishing date 2023-01-13
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
    ISSN 1520-5207
    ISSN (online) 1520-5207
    DOI 10.1021/acs.jpcb.2c07672
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  9. Article ; Online: Comparative Modeling and Analysis of Extremophilic D-Ala-D-Ala Carboxypeptidases.

    Diessner, Elizabeth M / Takahashi, Gemma R / Martin, Rachel W / Butts, Carter T

    Biomolecules

    2023  Volume 13, Issue 2

    Abstract: Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be ... ...

    Abstract Understanding the molecular adaptations of organisms to extreme environments requires a comparative analysis of protein structure, function, and dynamics across species found in different environmental conditions. Computational studies can be particularly useful in this pursuit, allowing exploratory studies of large numbers of proteins under different thermal and chemical conditions that would be infeasible to carry out experimentally. Here, we perform such a study of the MEROPS family S11, S12, and S13 proteases from psychophilic, mesophilic, and thermophilic bacteria. Using a combination of protein structure prediction, atomistic molecular dynamics, and trajectory analysis, we examine both conserved features and trends across thermal groups. Our findings suggest a number of hypotheses for experimental investigation.
    MeSH term(s) Extremophiles ; Proteins/metabolism ; Carboxypeptidases/metabolism ; Adaptation, Physiological
    Chemical Substances alanylalanine (2867-20-1) ; Proteins ; Carboxypeptidases (EC 3.4.-)
    Language English
    Publishing date 2023-02-09
    Publishing country Switzerland
    Document type Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, U.S. Gov't, Non-P.H.S.
    ZDB-ID 2701262-1
    ISSN 2218-273X ; 2218-273X
    ISSN (online) 2218-273X
    ISSN 2218-273X
    DOI 10.3390/biom13020328
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  10. Book ; Online: Rooted America

    Huang, Peng / Butts, Carter T.

    Immobility and Segregation of the Intercounty Migration Network

    2022  

    Abstract: Despite the popular narrative that the United States is a "land of mobility," the country may have become a "rooted America" after a decades-long decline in migration rates. This article interrogates the lingering question about the social forces that ... ...

    Abstract Despite the popular narrative that the United States is a "land of mobility," the country may have become a "rooted America" after a decades-long decline in migration rates. This article interrogates the lingering question about the social forces that limit migration, with an empirical focus on internal migration in the United States. We propose a systemic, network model of migration flows, combining demographic, economic, political, and geographic factors and network dependence structures that reflect the internal dynamics of migration systems. Using valued temporal exponential-family random graph models, we model the network of intercounty migration flows from 2011 to 2015. Our analysis reveals a pattern of segmented immobility, where fewer people migrate between counties with dissimilar political contexts, levels of urbanization, and racial compositions. Probing our model using "knockout experiments" suggests one would have observed approximately 4.6 million (27 percent) more intercounty migrants each year were the segmented immobility mechanisms inoperative. This article offers a systemic view of internal migration and reveals the social and political cleavages that underlie geographic immobility in the United States.
    Keywords Computer Science - Social and Information Networks ; Statistics - Applications
    Subject code 337
    Publishing date 2022-05-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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