| Member Profile : Carter Butts |
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 | Contact Information | Address: -Map Me-
Carter Butts
University of California, Irvine, Department of Sociology and Institute for Mathematical Behavioral Sciences
SSPA 2145
University of California, Irvine
Irvine, CA, United States 92697-5100
Phone : (949) 824-8591
E-mail : buttsc@uci.edu
Website : http://research.carterbutts.com/
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| Bibliographic Information |
Butts, C. (2008). network: a Package for Managing Relational Data in R. Journal of Statistical Software, 24 (2).
Butts, C. (2008). Social Network Analysis with sna. Journal of Statistical Software, 24 (6).
Butts, C. (2008). Social Networks: A Methodological Introduction. Asian Journal of Social Psychology, 11 (1), 13-41
Butts, C. (2007). Comment on Mark S. Handcock, Adrian E. Raftery, and Jeremy M. Tantrum, ``Model-based Clustering for Social Networks''. Journal of the Royal Statistical Society, Series A, 170 (2), 333-333
Butts, C. (2007). Models for Generalized Location Systems. Sociological Methodology, 37 (1), 283-348
Butts, C. (2007). Permutation Models for Relational Data. Sociological Methodology, 37 (1), 257-281
Butts, C., Petrescu-Prahova, M. & Cross, B. (2007). Responder Communication Networks in the World Trade Center Disaster: Implications for Modeling of Communication Within Emergency Settings. Journal of Mathematical Sociology, 31 (2), 121-147
Butts, C. (2007). Review of Carrington, Peter J.; Scott, John; and Wasserman, Stanley (eds.), Models and Methods in Social Network Analysis, Cambridge, Cambridge University Press.. Social Networks, 29 (4), 603-608
Butts, C. (2007). Statistical Mechanical Models for Social Systems. Bejan, Adria & Merkx, Gilber, (Eds.). Constructal Theory of Social Dynamics ( ed.). (pp. 197-224) New York: Springer
Butts, C. & Carley, K. (2007). Structural Change and Homeostasis in Organizations: A Decision-Theoretic Approach. Journal of Mathematical Sociology, 31 (4), 295-321
Butts, C. (2006). Exact Bounds for Degree Centralization. Social Networks, 28 (4), 283-296
Butts, C. & Carley, K. (2005). Some Simple Algorithms for Structural Comparison. Computational and Mathematical Organization Theory, 11 (4), 291-305
Butts, C. & Pixley, J. (2004). A Structural Approach to the Representation of Life History Data. Journal of Mathematical Sociology, 28 (2), 81-124
Butts, C. & Hilgeman, C. (2003). Inferring Potential Memetic Structure from Cross-Sectional Data: An Application to American Religious Beliefs. Journal of Memetics - Evolutionary Models of Information Transmission, 7 (2)
Butts, C. (2003). Network Inference, Error, and Informant (In)Accuracy: A Bayesian Approach. Social Networks, 25 (2), 103-140
Butts, C. (2003). Predictability of Large-scale Spatially Embedded Networks. Breiger, Ronal, Carley, Kathlee & Pattison, Philipp, (Eds.). Dynamic Social Network Modeling and Analysis: Workshop Summary and Papers ( ed.). (pp. 313-323) Washington, DC: National Academies Press
Butts, C. (2001). The Complexity of Social Networks: Theoretical and Empirical Findings. Social Networks, 23 (1), 31-71
Butts, C. (2000). An Axiomatic Approach to Network Complexity. Journal of Mathematical Sociology, 24 (4), 273-301
Fararo, T. & Butts, C. (1999). Advances in Generative Structuralism: Structured Agency and Multilevel Dynamics. Journal of Mathematical Sociology, 24 (1), 1-65
Anderson, B., Butts, C. & Carley, K. (1999). The Interaction of Size and Density with Graph Level Indices. Social Networks, 21 (3), 239-267
Butts, C. (1998). A Bayesian Model of Panic in Belief. Computational and Mathematical Organization Theory, 4 (4), 373-404
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| | | Software & Data | | Active Calendar Listings |
| R Packages for Network Analysis | (Software) |
| This page contains information on various scripts and packages for the analysis of network (and other data) withi the R statistical computing system. This includes core packages such as sna and network, as well as more specialized libraries such as nettheory. See also the statnet project web site (http://statnetproject.org). |
| Statnet Project | (Software) |
statnet is a suite of software packages for network analysis that implement recent advances in the statistical modeling of networks. The analytic framework is based on Exponential family Random Graph Models (ergm). statnet provides a comprehensive framework for ergm-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC) algorithm.
statnet has a different purpose than the excellent packages UCINET or Pajek; the focus is on statistical modeling of network data. The statistical modeling capabilities of statnet include ERGMs, latent space and latent cluster models. The packages are written in a combination of (the open-source statistical language) R and (ANSI standard) C, and are called from the R command line. And because it runs in the R package (www.r-project.org), you also have access to the full functionality of R, including the packages "network" and "sna." statnet has a command line interface, not a GUI, with a syntax that resembles R. |
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| | | Network Graduate Programs | | Network Courses |
| University of California, Irvine | Department of Department of Sociology |
| Program in Sociology, Social Network Specialization |
The University of California at Irvine is home to one of the premier research groups in the expanding field of social networks. With faculty in Sociology, Anthropology, Economics, Criminology, Law, and Society, Information and Computer Sciences, and the Graduate School of Management, UCI maintains a large and diverse community of network researchers with a wide range of substantive interests. The School of Social Sciences has had a Graduate Program in Social Networks for 20 years (it has granted 46 Ph.D.s since the mid 1980s). With an active community and numerous opportunities for research collaborations, UCI is an ideal place to study social networks.
The Sociology Department is a major hub of social network activity at UCI. We offer a unified program of graduate training in social networks, with a field specialization in the area and a core curriculum covering theoretical foundations, methodological approaches, and substantive applications. We also host a regular colloquium series and weekly network research meetings where graduate students and faculty discuss their on-going research projects. Graduate training in the field is supported by faculty in several departments and the Institute for Mathematical Behavioral Sciences.
Current social network research by faculty and graduate students covers a wide array of substantive topics, including: networks among responders to disasters; socio-spatial features of networks in high crime neighborhoods; effects of economic and social transformations on kinship and support networks in rural villages; social networks of immigrants; global city networks; international trade networks; and homophily in professional networks, to name a few. UCI served as the founding home to the flagship journal in the field, Social Networks, with Lin Freeman as editor. In recent years the social network group has hosted a number of special events including symposia, workshops, and co-sponsorship of international meetings (including the XXV International Sunbelt Social Network Conference, and the US/Japan Mathematical Sociology Conference). |
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| Social Networks | | University of California, Irvine, Department of Sociology |
| Study of the causes and consequences of patterned interactions among social entities is the domain of the social network field. This course provides a hands-on introduction to some of the basic concepts and methods of network analysis, as well as a sampling of classic and modern research findings regarding the properties of social networks. By the end of this course, each student will have an understanding of the above topics, as well as experience with the collection and analysis of network data using modern computational tools. The course will culminate in a group research project, in which each student will be involved in the collection, analysis, and presentation of network data on a topic of their choosing. |
| Informant Accuracy | | University of California, Irvine, Department of Sociology |
| Ethnographic, archival, and survey research frequently depends upon individual accounts to reconstruct historical events, cultural conventions, past behavior, social structure, and the like. In such settings, the sources of these accounts (i.e., informants) act as the ``measurement devices'' through which the social scientist probes the system under study. While human informants can yield information which is difficult or impossible to obtain through alternate means, their accounts are subject to various kinds of error. Understanding the determinants of such error is thus of substantial importance in conducting informant-based research. This class surveys key findings from the literature on informant accuracy, and introduces a number of methods for estimating and reducing the impact of error on subsequent analyses. Specific applications examined include cultural domain analysis, the estimation of competency, and network inference. |
| Networks and Information Transmission | | University of California, Irvine, Department of Sociology |
| From its earliest beginnings, communication -- and, in particular, the flow of information -- has been one of the core themes of the social network field. This course provides an introduction to current and past research on communication and information transmission within interpersonal networks. Coverage anges from the micro-processes involved in information acquisition and recall to the macro-level phenomena of diffusion at the population level, with the meso-level process of local communication also being considered. Specific topics covered include cognitive and affective effects on communication, information corruption due to serial transmission, rumors and disasters, memetics, and information seeking behavior. Organizational and policy implications are also discussed. In addition to reviewing relevant literature, students in this class develop their own research projects relating to the course topic, and opportunities are provided to present this work to the class as a whole. |
| Networks and Organizations | | University of California, Irvine, Department of Sociology |
| Structural perspectives have long played a major role in the study of organizations, and organizational research has provided a corresponding impetus for the development of modern network analysis. This course will provide an introduction to some of the major areas of research at the intersection of these two fields. The approach taken is interdisciplinary, bringing together work in sociology, management science, organizational behavior, and economics; emphasis is on predictive (i.e., scientific) research, but some normative (i.e., engineering) issues will be considered as well. Specific topics covered include firm size, organizational design, diffusion and influence processes, competition, exchange processes, and organizational learning. In addition to reviewing relevant literature, students in this class develop their own research projects relating to the course topic, and opportunities are provided to present this work to the class as a whole. |
| Network Theory | | University of California, Irvine, Department of Sociology |
| This course provides an introduction to the basic principles and classic themes dominating theoretical work in the social network field. Specific topics covered include baseline network models, homophily and propinquity, theories of exchange and power, balance theory, models of diffusion and social influence, equivalence, and cohesion. The approach taken to the material is hands-on: homework assignments focus on the active use of theory to make specific predictions about social structures and processes. By the end of the class, each student should understand the basic concepts used to represent relational structure, should be familiar with several of the major theoretical programmes in the network field, and should be able to apply these theories to novel scientific problems. |
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| | | Jobs Posted | | Sunbelt Submissions |
Postdoctoral Scholar Position on Statistical Methods for Network Analysis - Last Updated May 31, 2011
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| Sunbelt XXIX - March 10 to March 15, 2009 - Bahia Hotel | | Workshop : The Practice of Exponential family Random Graph (ERG or p*) modeling: A Practical introduction to Exponential family Random Graph Modeling in statnet: an R-based environment for statistical analysis and simulation of social networks |
| This workshop is a tutorial on exponential random graph models for social networks, emphasizing a hands-on approach to fitting these models to empirical data. This workshop will provide a hands-on tutorial to statnet, a statistical package for the visualization, analysis and simulation of social network data. The modeling capabilities of statnet include the class of exponential random graph (ERG) models. These models recognize the complex dependencies within relational data structures, and provide a very flexible framework for representing them. Examples include degree distributions and stars, attribute-based mixing patterns, triadic patterns that lead to clustering, shared partner distributions, the new specifications in Snijders et. Al. 2006, and other systematic network configurations. statnet has a coherent and flexible user interface and can handle relatively large networks (~3,000 is the largest network we have estimated models for), and it has very efficient algorithms for data manipulation and analysis. The package provides tools for both model estimation and model-based network simulation, with visualization, tools for inference and validation, and goodness of fit diagnostics. The package is written for the R statistical computing environment, so it runs on any computing platform that supports R (Windows, Unix/Linux, Mac), it is freely available through the Comprehensive R Archive Network (CRAN), and it has a seamless interface to SNA (an R package for traditional network analysis written by Carter Butts). |
| SunBelt XXVIII - January 22 to January 27, 2008 - Trade Winds Beach Resort
http://www.tradewindsresort.com/
St. Pete Beach | | Workshop : 5. The Practice of Exponential family Random Graph (ERG or p*) modeling. |
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| Sunbelt XXX - June 29 to July 04, 2010 - Riva del Garda Fierecongressi | | Abstract : A Perfect Sampling Method for Random Graph Models |
| Generation of deviates from random graph models with non-trivial edge dependence is an increasingly important problem in the social and biological sciences. In recent years, work on this problem has been greatly facilitated by the use of discrete exponential families to parameterize random graph models, and by the availability of associated Markov chain Monte Carlo methods for approximate simulation of these families. Here, I introduce a method which allows perfect sampling from random graph models in exponential family form (aka ``exponential random graph'' models), using a variant of Coupling From The Past. I illustrate the use of the method via an application to the Markov graphs, a family of considerable importance within the social network literature. Applications of the method to other common cases is also discussed. |
| Workshop : Introduction to Exponential-family Random Graph (ERG or p*) modeling with statnet |
| This workshop is a tutorial on exponential random graph models for social networks, emphasizing a hands-on approach to fitting these models to empirical data. This workshop will provide a hands-on tutorial to statnet, a statistical package for the visualization, analysis and simulation of social network data. The modeling capabilities of statnet include the class of exponential random graph (ERG) models. These models recognize the complex dependencies within relational data structures, and provide a very flexible framework for representing them. Examples include degree distributions and stars, attribute-based mixing patterns, triadic patterns that lead to clustering, shared partner distributions, the new specifications in Snijders et. Al. 2006, and other systematic network configurations. statnet has a coherent and flexible user interface and can handle relatively large networks (~3,000 is the largest network we have estimated models for), and it has very efficient algorithms for data manipulation and analysis. The package provides tools for both model estimation and model-based network simulation, with visualization, tools for inference and validation, and goodness of fit diagnostics. The package is written for the R statistical computing environment, so it runs on any computing platform that supports R (Windows, Unix/Linux, Mac), it is freely available through the Comprehensive R Archive Network (CRAN), and it has a seamless interface to SNA (an R package for traditional network analysis written by Carter Butts). |
| Sunbelt XXXI - February 08 to February 13, 2011 - Trade Winds Beach Resort
http://www.tradewindsresort.com/
St. Pete Beach | | Abstract : A Technique for Analyzing ERGM Behavior Using Bernoulli Graphs |
| The use of discrete exponential families has revolutionized the modeling of networks with properties such as heterogeneity and/or complex dependence among edges. Such exponential-family random graph models (or ERGMs) constitute a general language for describing distributions of networks, and are increasingly widely employed both within and beyond the social sciences. While the generality of the ERGM framework is appealing, few techniques other than simulation have been available for studying the behavior of models with non-trivial edgewise dependence. Random graph models with independent edges (i.e., the Bernoulli graphs), on the other hand, are well-studied, and a large literature exists regarding their properties. Here, I demonstrate a method for leveraging this knowledge by constructing families of Bernoulli graphs that bound the behavior of general ERGMs in a well-defined sense. By examining the behavior of these Bernoulli graph bounds, one can thus analyze many properties of the associated ERGMs. Several applications of this method to the study of complex network models are discussed, including the identification of models that avoid asymptotic degeneracy and robustness testing for large-scale network models based on geographical covariates. |
| Workshop : Moving Beyond Descriptives: An Introduction to Basic Network Statistics with statnet |
| This workshop will serve as an introduction to the use of basic statistical methods for network analysis within the R/statnet platform. The approach taken is practical rather than theoretical, with emphasis on simple, robust methods for hypothesis testing and exploratory data analysis of single and multi-network data sets. Topics will include: tests for marginal relationships between node or graph-level indices and covariates; network autocorrelation models; Monte Carlo tests for structural biases; network correlation and regression; and exploratory multivariate analysis of multi-network data sets. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Familiarity with the basic concepts of descriptive network analysis (e.g., centrality scores, network visualization) is strongly recommended. |
| Sunbelt XXVI - April 25 to April 30, 2006 - Vancouver | | Workshop : 4b. statnet: a new R-based program for fitting ERG and other statistical models for social networks |
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| Sunbelt XXXII - March 12 to March 18, 2012 - Crowne Plaza Hotel
https://resweb.passkey.com/go/INSNASunbelt
Redondo Beach | | Workshop : Exponential-family Random Graph (ERG or p*) Modeling with statnet* |
This workshop session provides a tutorial on exponential-family random graph models (ERGMs) for social networks, emphasizing a hands-on approach to fitting these models to empirical data. This session will provide a hands-on tutorial to ERGM modeling within the R/statnet platform. The ERGM framework allows for the parametrization, fitting, and simulation from models that incorporate the complex dependencies within relational data structures, and provides an extremely general and flexible means of representing them. Topics covered within this session
include: an overview of the ERGM framework; defining and fitting models to empirical data; interpretation of model coefficients; goodness-of-fit and model adequacy checking; simulation of networks using ERG models; and degeneracy assessment. Attendees are expected to have had some prior exposure to R, but extensive experience is not assumed. Familiarity with basic descriptive network concepts and basic statistical methods for network analysis within the R/statnet platform (e.g., from the \\\"Introduction to Network Analysis with R and statnet\\\"
and \\\"Basic Network Statistics with statnet\\\" workshop sessions) is recommended.
statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac). statnet packages can be used to handle a wide range of simulation and analysis tasks, including support for large networks, statistical network models, network dynamics, and missing data.
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| Workshop : Modeling Relational Event Dynamics with statnet* |
This workshop session will provide an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within R/statnet platform. We will begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We will then discuss estimation of dyadic and more general relational event models using the relevent package, with an emphasis on hands-on applications of the methods and interpretation of results. Using the informR package, we will then show how to construct models for spell data, and data involving multiple event types. Attendees are expected to have had some prior exposure to R and statnet, and completion of the \\\"Introduction to Network Analysis with R and statnet\\\" workshop session is suggested (but not required) as preparation for this session. Familiarity with parametric statistical methods is strongly recommended, and some knowledge of hazard or survival analysis will be helpful.
statnet is a collection of packages for the R statistical computing system that supports the representation, manipulation, visualization, modeling, simulation, and analysis of relational data. statnet packages are contributed by a team of volunteer developers, and are made freely available under the GNU Public License. These packages are written for the R statistical computing environment, and can be used with any computing platform that supports R (including Windows, Linux, and Mac). statnet packages can be used to handle a wide range of simulation and analysis tasks, including support for large networks, statistical network models, network dynamics, and missing data. |
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