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Logic Models and Logistic Regressions for Social Networks
 Multivariate Relations, British Journal of mathematical and Statistical Psychology
, 1999
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A p* primer: logit models for social networks
 SOCIAL NETWORKS
, 1999
"... A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others wHolland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions ..."
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Cited by 78 (1 self)
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A major criticism of the statistical models for analyzing social networks developed by Holland, Leinhardt, and others wHolland, P.W., Leinhardt, S., 1977. Notes on the statistical analysis of social network data; Holland, P.W., Leinhardt, S., 1981. An exponential family of probability distributions for directed graphs. Journal of the American Statistical Association. 76, pp. 33–65 Ž with discussion.; Fienberg, S.E., Wasserman,
ergm: A Package to Fit, Simulate and Diagnose ExponentialFamily Models for Networks
 Journal of Statistical Software
, 2008
"... We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponentialfamily random graph models (ERGMs): estimation, simulation, and goodness of fit. More precis ..."
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Cited by 52 (7 self)
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We describe some of the capabilities of the ergm package and the statistical theory underlying it. This package contains tools for accomplishing three important, and interrelated, tasks involving exponentialfamily random graph models (ERGMs): estimation, simulation, and goodness of fit. More precisely, 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 fitted ERGM does at capturing characteristics of a particular network data set.
Identifying Community Structures from Network Data via Maximum Likelihood Methods
, 2005
"... In many economic situations it is of interest to know who interacts with whom. In international trade, for example, some theories predict that members of certaing groups will have a higher probability of trading with each other than with those in other groups. Based on a model of within and across g ..."
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Cited by 40 (10 self)
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In many economic situations it is of interest to know who interacts with whom. In international trade, for example, some theories predict that members of certaing groups will have a higher probability of trading with each other than with those in other groups. Based on a model of within and across group interactions, we describe, characterize, and implement, a new method for identifying trading or community structures from network data. The method is based on maximum likelihood estimation, a standard statistical tool.
p2: a random effects model with covariates for directed graphs
 Statistica Neerlandica
, 2004
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The Effects of R&D Team Colocation on Communication Patterns among R&D, Marketing, and Manufacturing
 Management Science
, 1998
"... Reducing the physical distance among R&D engineers and between R&D and marketing is widely believed to result in more frequent communication, and hence higher product development performance. However, the empirical evidence for the effect of colocation on communication frequency is problema ..."
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Cited by 33 (0 self)
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Reducing the physical distance among R&D engineers and between R&D and marketing is widely believed to result in more frequent communication, and hence higher product development performance. However, the empirical evidence for the effect of colocation on communication frequency is problematic for two reasons: (1) the evidence often features either little contextual realism or doubtful internal validity, and (2) the analysis does not deal with the statistical problems typical of network data. Our study avoids the first problem by using sequential network data collected from a quasiexperiment at an industrial company that regrouped its R&D teams into a new facility. We avoid the second problem by using Wasserman and Iacobucci's (1988) method for the statistical analysis of sequential network data. Our results show that communication among R&D teams was enhanced after colocating these teams. Surprisingly, communication frequency between R&D and marketing was not affected by the increa...