Models for longitudinal network datain

By Tom A. B. Snijders. 2005

490 citations recorded by the source corpus.

Subfields: Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/261fb96bd71dc18f91d4d839afd163316498fd75

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In Statistical and Nonlinear Physics

Citations in the corpus

Network analysis in conservation biogeography: challenges and opportunities (2010, methodology): “A second example is that of the application of agent-based simulation approaches at the level of individual nodes (c.f. Snijders, 2005).”
Beyond dyadic interdependence: Actor-oriented models for co-evolving social networks and individual behaviors (2007, methodology): “This was generalized by Snijders (2001, 2005) to stochastic actor-oriented models, which are dynamic network models that also account for transitivity and other three- or more-actor dependencies.”
Popularity as a moderator of peer selection and socialization of adolescent alcohol, marijuana, and tobacco use (2013, methodology): “The primary analyses consisted of two stochastic actor-based models of network-behavioral dynamics developed by Snijders (2005), Snijders et al.”
Networks in Political Science: Back to the Future (2011, background): “Longitudinal data are also powerful in discerning causal direction, for which the observation of temporal precedence may allow an inference of causal order (Snijders 2005; Christakis and Fowler 2007).”
A Survey of Statistical Network Models (2009, methodology): “We begin by providing a quick reminder of continuous Markov processes, borrowing notation from [275].”
Applying SIENA: An illustrative analysis of the co-evolution of adolescents’ friendship networks, taste in music, and alcohol consumption (2006, methodology): “ Snijders (1996, 2001, 2005) introduced a family of stochastic, actor-driven models for the evolution of social networks ‘alone’ (i.e., not yet allowing for coevolving individual dimensions).”
The analysis of social networks (2008, background): “A much more elaborate statistical model for network evolution is the actor-oriented model proposed by (Snijders 1996, 2001, 2005).”
Transformations in adolescent peer networks (2011, methodology): “Due to the unobserved nature of the exact sequence of changes connecting observation moments, simulation-based inference is necessary for estimating the model parameters (Snijders, 2001, 2005; Snijders et al. 2007, 2010).”
4. A Relational Event Framework for Social Action (2008, methodology): “In this respect, u is directly analogous to the elements of the objective/gratification functions of the dynamic network models of Snijders (1996, 2005), and to the sufficient statistics invoked in the parameterization of exponential random graph (aka ERG or p ∗ ) (Wasserman and Robins 2005;…”
DYNAMIC NETWORKS AND BEHAVIOR: SEPARATING SELECTION FROM INFLUENCE: separating selection from influence (2010, methodology): “(2007), which is an extension of earlier modeling work by SNIJDERS (2001, 2005) for networks without co-evolving behavioral dimensions.”
Introduction to stochastic actor-based models for network dynamics (2010, background): “More precise explanations can be found in Snijders (2001, 2005) and Snijders, Steglich, and Schweinberger (2007).”
Social Networks and Causal Inference (2013, methodology): “…observational longitudinal data with repeated measures has been used to attempt to draw conclusions about social influence on a network, even when data from only a single social network is available (Christakis and Fowler 2007, 2008, 2013; Snijders 2001, 2005; Steglich et al. 2010).”
Manual for SIENA version 2.1 (2005, methodology): “…tests according to Schweinberger (2005), see Section 7; 3. the possibility to analyse dynamics of non-directed networks, according to Snijders (2005b); 4. statistical Monte Carlo studies, see Section 19; 5. extension of the specifications of the exponential random graph (“p∗”) model…”
Social network analysis: developments, advances, and prospects (2010, background): “Tom Snijders (Snijders and van Duijn 1997; Snijders 2001, 2005) has developed an approach that sees the incremental adjustment of individual action to the changing network structure, resulting in a continuous—but often non-linear— process of network development.”