This is a data set of 18 women observed over a nine-month period. During that period, various subsets of these women had met in a series of 14 informal social events. The data recored which women met for which events. The data is originally from Davis, Gardner and Gardner (1941) via UCINET and stored as a
networkDynamic data object
This version includes event timings according to the chart extracted by Berger-Wolf from Davis, et al. stored as instantaneous events in numeric POSIX time. In both networks the vertices are marked as 'always active', although the actuall availibility for event membership is not known. This version includes the two (overlapping) group classifications reported by Davis, et al. (via Freeman). The name "Myra" is corrected from the latentnet version of the dataset.
davisDyn object is a bi-partite network relating the actors to the events.
davisActorsDyn is one-mode projection of the bipartite network to create a network of the women mutually connected by the events they attend.
The documentation below is taken from Freeman (2003) in his usual lucid description. See the reference to the paper below:
In the 1930s, five ethnographers, Allison Davis, Elizabeth Stubbs Davis, Burleigh B. Gardner, Mary R. Gardner and J. G. St. Clair Drake, collected data on stratification in Natchez, Mississippi (Warner, 1988, p. 93). They produced the book cited below [DGG] that reported a comparative study of social class in black and in white society. One element of this work involved examining the correspondence between people's social class levels and their patterns of informal interaction. DGG was concerned with the issue of how much the informal contacts made by individuals were established solely (or primarily) with others at approximately their own class levels. To address this question the authors collected data on social events and examined people's patterns of informal contacts.
In particular, they collected systematic data on the social activities of 18 women whom they observed over a nine-month period. During that period, various subsets of these women had met in a series of 14 informal social events. The participation of women in events was uncovered using "interviews, the records of participant observers, guest lists, and the newspapers"" (DGG, p. 149). Homans (1950, p. 82), who presumably had been in touch with the research team, reported that the data reflect joint activities like, "a day's work behind the counter of a store, a meeting of a women's club, a church supper, a card party, a supper party, a meeting of the Parent-Teacher Association, etc."
This data set has several interesting properties. It is small and manageable. It embodies a relatively simple structural pattern, one in which, according to DGG, the women seemed to organize themselves into two more or less distinct groups. Moreover, they reported that the positions - core and peripheral - of the members of these groups could also be determined in terms of the ways in which different women had been involved in group activities. At the same time, the DGG data set is complicated enough that some of the details of its patterning are less than obvious. As Homans (1950, p. 84) put it, "The pattern is frayed at the edges." And, finally, this data set comes to us in a two-mode "woman by event" form. Thus, it provides an opportunity to explore methods designed for direct application to two-mode data. But at the same time, it can easily be transformed into two one-mode matrices (woman by woman or event by event) that can be examined using tools for one-mode analysis.
Because of these properties, this DGG data set has become something of a touchstone for comparing analytic methods in social network analysis. Davis, Gardner and Gardner presented an intuitive interpretation of the data, based in part on their ethnographic experience in the community. Then the DGG data set was picked up by Homans (1950) who provided an alternative intuitive interpretation. In 1972, Phillips and Conviser used an analytic tool, based on information theory, that provided a systematic way to reexamine the DGG data. Since then, this data set has been analyzed again and again. It reappears whenever any network analyst wants to explore the utility of some new tool for analyzing data.
Unknown. Based on original publication date, the data are believed to be public domain and have been previously widely circulated in various accademic sources.
This dataset was re-assembled from multiple sources:
Davis, A., Gardner, B. B. and M. R. Gardner (1941) Deep South, Chicago: The University of Chicago Press.
Breiger R. (1974). The duality of persons and groups. Social Forces, 53, 181-190
Linton C. Freeman (2003). Finding Social Groups: A Meta-Analysis of the Southern Women Data, In Ronald Breiger, Kathleen Carley and Philippa Pattison, eds. Dynamic Social Network Modeling and Analysis. Washington: The National Academies Press. http://intersci.ss.uci.edu/wiki/pub/FreemanSouthernWomen85.pdf
Berger-Wolf, T. Y., & Saia, J. (2006). A framework for analysis of dynamic social networks. In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 523-528). ACM. http://www.cs.unm.edu/~saia/papers/kdd.pdf
Krivitsky P and Handcock M (2015). _latentnet: Latent Position and Cluster Models for Statistical Networks_. The Statnet Project (<URL: http://www.statnet.org>). R package version 2.7.1, <URL: http://CRAN.R-project.org/package=latentnet>.
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Loading required package: networkDynamic Loading required package: network network: Classes for Relational Data Version 1.15 created on 2019-04-01. copyright (c) 2005, Carter T. Butts, University of California-Irvine Mark S. Handcock, University of California -- Los Angeles David R. Hunter, Penn State University Martina Morris, University of Washington Skye Bender-deMoll, University of Washington For citation information, type citation("network"). Type help("network-package") to get started. networkDynamic: version 0.10.0, created on 2019-04-04 Copyright (c) 2019, Carter T. Butts, University of California -- Irvine Ayn Leslie-Cook, University of Washington Pavel N. Krivitsky, University of Wollongong Skye Bender-deMoll, University of Washington with contributions from Zack Almquist, University of California -- Irvine David R. Hunter, Penn State University Li Wang Kirk Li, University of Washington Steven M. Goodreau, University of Washington Jeffrey Horner Martina Morris, University of Washington Based on "statnet" project software (statnet.org). For license and citation information see statnet.org/attribution or type citation("networkDynamic"). NetworkDynamic properties: distinct change times: 14 maximal time range: -1257696000 until -1234281600 Includes optional net.obs.period attribute: Network observation period info: Number of observation spells: 1 Maximal time range observed: -1257696000 until -1234281600 Temporal mode: continuous Time unit: posix Suggested time increment: NA Network attributes: vertices = 32 directed = FALSE hyper = FALSE loops = FALSE multiple = FALSE bipartite = 18 net.obs.period: (not shown) total edges= 89 missing edges= 0 non-missing edges= 89 Vertex attribute names: DGGgroup1 DGGgroup2 active vertex.names Edge attribute names: active  "1930-02-23 08:00:00 UTC" "1930-02-25 08:00:00 UTC"  "1930-03-02 08:00:00 UTC" "1930-03-15 08:00:00 UTC"  "1930-04-07 08:00:00 UTC" "1930-04-08 08:00:00 UTC"  "1930-04-12 08:00:00 UTC" "1930-05-19 08:00:00 UTC"  "1930-06-10 08:00:00 UTC" "1930-06-27 08:00:00 UTC"  "1930-08-03 08:00:00 UTC" "1930-09-16 08:00:00 UTC"  "1930-09-26 08:00:00 UTC" "1930-11-21 08:00:00 UTC" NetworkDynamic properties: distinct change times: 14 maximal time range: -1257696000 until -1234281600 Includes optional net.obs.period attribute: Network observation period info: Number of observation spells: 1 Maximal time range observed: -1257696000 until -1234281600 Temporal mode: continuous Time unit: posix Suggested time increment: NA Network attributes: vertices = 18 directed = FALSE hyper = FALSE loops = FALSE multiple = FALSE bipartite = FALSE net.obs.period: (not shown) total edges= 139 missing edges= 0 non-missing edges= 139 Vertex attribute names: DGGgroup1 DGGgroup2 vertex.names Edge attribute names: active
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