Mark S. Handcock
Contents |
Home
- Mark Handcock home page at UCLA, Professor of Statistics at UCLA.
- Mark Handcock former home page at UW Seattle
event from InterSciWiki
Human Sciences and Complexity - UC Four Campus Complexity Videoconference Friday February 22, 2008 HANDCOCK: "A simple model for complex networks with arbitrary degree distribution and clustering" powerpoint pdf, with Martina Morris. InterSci wiki R Paper Examples: Using R to replicate a published study
Running the simple model for complex networks with arbitrary degree distribution and clustering in R
Application
David Krackhardt and Mark S. Handcock 2007 Heider vs Simmel: Emergent Features in Dynamic Structures Lecture Notes in Computer Science 4503: 14-27.
Mark S. Handcock was Professor and Chair of Statistics at the University of Washington and a core faculty member of the Center for Statistics and the Social Sciences. His work focuses on the development of statistical models for the analysis of social network data, epidemiology, spatial processes and demography. He received his Ph.D. from the University of Chicago. He is a principal author of the statnet R code, which focuses on statistical modeling of network data through ergms, latent space and latent cluster models.
Statnet
Mark S. Handcock, David R. Hunter, Carter T. Butts, Steven M. Goodreau, Martina Morris. 2008 statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data Journal of Statistical Software 24(1):1-11.
New specifications for exponential random graph models. Sociological Methodology. Snijders, T.A.B., Steglich, C.E.G., Schweinberger, M., Huisman, M., 2005
The R software package statnet: software tools for the representation, visualization, analysis and simulation of social network data.
The R software package latentnet: software to fit and evaluate latent position and cluster models for statistical networks.
The preface, data sets, and software to implement the methods in Mark S. Handcock and Martina Morris, 1999, Relative Distribution Methods in the Social Sciences, are available from the Relative Distribution website. Review: http://sankhya.isical.ac.in/search/62b3/62b3rev3.pdf
Working papers
CSSS Working Paper no. 42 ([PDF(544K bytes )] "New specifications for exponential random graph models" Tom A.B. Snijders, Philippa E. Pattison, Garry L. Robbins and Mark S. Handcock
See also
- S. Hanneke and E.P Xing, Discrete Temporal Models of Social Networks, In proceedings of the Workshop on Statistical Network Analysis, the 23rd International Conference on Machine Learning (ICML-SNA 2006).
