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Identifying cohesive subgroups *
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2003 |
| Abstract | Cohesive subgroups have always represented an important construct for sociologists who study individuals and organizations. In this article, I apply recent advances in the statistical modell ing of social network data to the task of identifying cohesive subgroups from social network data. Further, through simulated data, I describe a process for obtaining the probability that a given sample of data could have been obtained from a network in which actors were no more likely to engage in interaction with subgroup members than with members of other subgroups. I obtain the probability for a specific data set, and then, through further simulations, develop a model which can be applied to future data sets. Also through simulated data, I characterize the extent to which a simple hill-climbing algorithm recovers known subgroup memberships. I apply the algorithm to data indicating the extent of professional discussion among teachers in a high school, and I show the relationship between membership in cohesive subgroups and teachers ' orientations towards teaching. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://msu.edu/user/k/e/kenfrank/web/papers/identifying%20cohesive%20subgroups.pdf |
| Alternate Webpage(s) | https://www.msu.edu/~kenfrank/papers/identifying%20cohesive%20subgroups.pdf |
| Language | English |
| Access Restriction | Open |
| Subject Keyword | Algorithm Hill climbing Mental Orientation Simulation Social network Subgroup A Nepoviruses V-Model |
| Content Type | Text |
| Resource Type | Article |
