A Relational Event Approach to Modeling Behavioral Dynamics

This chapter provides an introduction to the analysis of relational event data (i.e., actions, interactions, or other events involving multiple actors that occur over time) within the R/statnet platform. We begin by reviewing the basics of relational event modeling, with an emphasis on models with piecewise constant hazards. We then discuss estimation for 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. 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).

[1]  John R. Hipp,et al.  Coevolution of adolescent friendship networks and smoking and drinking behaviors with consideration of parental influence. , 2016, Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors.

[2]  Zack W. Almquist,et al.  Logistic Network Regression for Scalable Analysis of Networks with Joint Edge/Vertex Dynamics , 2011, Sociological methodology.

[3]  Melinda Mills,et al.  Introducing Survival and Event History Analysis , 2011 .

[4]  M. Tranmer,et al.  Using the relational event model (REM) to investigate the temporal dynamics of animal social networks , 2015, Animal Behaviour.

[5]  David R. Gibson Participation Shifts: Order and Differentiation in Group Conversation , 2003 .

[6]  A. Rapoport Outline of a probabilistic approach to animal sociology. , 1949, The Bulletin of mathematical biophysics.

[7]  Daniel A. McFarland,et al.  The Art and Science of Dynamic Network Visualization , 2006, J. Soc. Struct..

[8]  Padhraic Smyth,et al.  Hierarchical Models for Relational Event Sequences , 2012, 1207.7306.

[9]  Noah E. Friedkin,et al.  A Structural Theory of Social Influence: List of Tables and Figures , 1998 .

[10]  D. L. Swain,et al.  Time is of the essence: an application of a relational event model for animal social networks , 2015, Behavioral Ecology and Sociobiology.

[11]  Carter T Butts,et al.  Constructing and Modifying Sequence Statistics for relevent Using informR in 𝖱. , 2015, Journal of statistical software.

[12]  Pavel N Krivitsky,et al.  A separable model for dynamic networks , 2010, Journal of the Royal Statistical Society. Series B, Statistical methodology.

[13]  Noah E. Friedkin,et al.  A Structural Theory of Social Influence: List of Tables and Figures , 1998 .

[14]  C. Butts,et al.  Responder Communication Networks in the World Trade Center Disaster: Implications for Modeling of Communication Within Emergency Settings , 2007 .

[15]  Padhraic Smyth,et al.  Stochastic blockmodeling of relational event dynamics , 2013, AISTATS.

[16]  M. Macy,et al.  Complex Contagions and the Weakness of Long Ties1 , 2007, American Journal of Sociology.

[17]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[18]  S. Wasserman,et al.  Models and Methods in Social Network Analysis: An Introduction to Random Graphs, Dependence Graphs, and p * , 2005 .

[19]  T. Snijders,et al.  Bayesian inference for dynamic social network data , 2007 .

[20]  P. Pattison,et al.  Random graph models for temporal processes in social networks , 2001 .

[21]  Stanley Wasserman,et al.  Social Network Analysis: Methods and Applications , 1994, Structural analysis in the social sciences.

[22]  T. Snijders The statistical evaluation of social network dynamics , 2001 .

[23]  Leslie A. DeChurch,et al.  Once upon a time , 2016 .

[24]  Carter T. Butts,et al.  4. A Relational Event Framework for Social Action , 2008 .

[25]  T. Snijders Stochastic actor-oriented models for network change , 1996 .

[26]  Leslie A. DeChurch,et al.  Once upon a time : Understanding team dynamics as relational event networks , 2015 .

[27]  Hai Liang,et al.  The Organizational Principles of Online Political Discussion: A Relational Event Stream Model for Analysis of Web Forum Deliberation , 2014 .

[28]  M. Requena HANS-PETER BLOSSFELD y GÖTZ ROHWER: Techniques of Event History Modeling. New Approaches to Causal Analysis. Mah- wah (New Jersey) y Londres, Lawrence Erlbaum Associates, segunda edición, 2002 , 2018 .

[29]  Daniel A. McFarland Student Resistance: How the Formal and Informal Organization of Classrooms Facilitate Everyday Forms of Student Defiance1 , 2001, American Journal of Sociology.

[30]  F. Heider ATTITUDES AND COGNITIVE ORGANIZATION , 1977 .

[31]  C. T. Butts,et al.  Revisiting the Foundations of Network Analysis , 2009, Science.

[32]  Mark S. Granovetter The Strength of Weak Ties , 1973, American Journal of Sociology.

[33]  Noah E. Friedkin A Structural Theory of Social Influence: Measures of the Theoretical Constructs , 1998 .

[34]  Karl Ulrich Mayer,et al.  Event History Analysis in Life Course Research , 1990 .

[35]  Brian D. M. Tom Techniques of Event History Modeling: New Approaches to Causal Analysis , 2003 .

[36]  Carter T. Butts,et al.  Emergent Coordinators in the World Trade Center Disaster , 2005, International Journal of Mass Emergencies & Disasters.

[37]  Derek de Solla Price,et al.  A general theory of bibliometric and other cumulative advantage processes , 1976, J. Am. Soc. Inf. Sci..

[38]  A. Rapoport Outline of a probabilistic approach to animal sociology. , 1949, The Bulletin of mathematical biophysics.

[39]  John R. Hipp,et al.  Simulating Dynamic Network Models and Adolescent Smoking: The Impact of Varying Peer Influence and Peer Selection. , 2015, American journal of public health.