M
Mark S. Handcock
Researcher at University of California, Los Angeles
Publications - 152
Citations - 18829
Mark S. Handcock is an academic researcher from University of California, Los Angeles. The author has contributed to research in topics: Population & Exponential random graph models. The author has an hindex of 53, co-authored 143 publications receiving 16990 citations. Previous affiliations of Mark S. Handcock include National Institute on Drug Abuse & University of California.
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Latent Space Approaches to Social Network Analysis
TL;DR: This work develops a class of models where the probability of a relation between actors depends on the positions of individuals in an unobserved “social space,” and proposes Markov chain Monte Carlo procedures for making inference on latent positions and the effects of observed covariates.
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New specifications for exponential random graph models
TL;DR: It is concluded that the new specifications of exponential random graph models increase the range and applicability of the ERGM as a tool for the statistical analysis of social networks.
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ergm: A Package to Fit, Simulate and Diagnose Exponential-Family Models for Networks.
TL;DR: Ergm has the capability of approximating a maximum likelihood estimator for an ERGM given a network data set; simulating new network data sets from a fitted ERGM using Markov chain Monte Carlo; and assessing how well a fittedERGM does at capturing characteristics of a particular networkData set.
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statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data.
TL;DR: Statnet is a suite of software packages for statistical network analysis that provides a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model- based network simulation, and network visualization.
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Recent developments in exponential random graph (p*) models for social networks
TL;DR: The inclusion of a new higher order transitivity statistic allows estimation of parameters of exponential graph models for many (but not all) cases where it is impossible to estimate parameters of homogeneous Markov graph models.