The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter

@article{Fujimoto2011TheNA,
  title={The network autocorrelation model using two-mode data: Affiliation exposure and potential bias in the autocorrelation parameter},
  author={Kayo Fujimoto and Chih-Ping Chou and Thomas W. Valente},
  journal={Social networks},
  year={2011},
  volume={33 3},
  pages={
          231-243
        }
}
Abstract The network autocorrelation model has been a workhorse for modeling network influences on individual behavior. The standard network approaches to mapping social influence using network measures, however, are limited to specifying an influence weight matrix ( W ) based on a single mode network. Additionally, it has been demonstrated that the estimate of the autocorrelation parameter ρ of the network effect tends to be negatively biased as the density in W matrix increases. The current… CONTINUE READING
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