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Techniques: Dichotomizing a Network Cover

Techniques: Dichotomizing a Network

Open Access
|Jan 2019

Figures & Tables

Figure 1

DGG Women by Women dataset dichotomized above 1.
DGG Women by Women dataset dichotomized above 1.

Figure 2

DGG Women by Women dataset dichotomized above 2.
DGG Women by Women dataset dichotomized above 2.

Figure 3

DGG Women by Women dataset dichotomized above 3.
DGG Women by Women dataset dichotomized above 3.

Figure 4

BKS FRATERNITY dataset dichotomized above 0.
BKS FRATERNITY dataset dichotomized above 0.

Figure 5

BKS FRATERNITY dataset dichotomized above 2.
BKS FRATERNITY dataset dichotomized above 2.

Figure 6

BKS FRATERNITY dataset dichotomized above 4.
BKS FRATERNITY dataset dichotomized above 4.

Figure 7

BKS FRATERNITY dataset dichotomized above 6.
BKS FRATERNITY dataset dichotomized above 6.

Figure 8

DGG Women by Women dataset dichotomized at 4.
DGG Women by Women dataset dichotomized at 4.

Figure 9

DGG Women by Women dataset dichotomized at 3. Strong ties in bold.
DGG Women by Women dataset dichotomized at 3. Strong ties in bold.

Figure A1

Screenshot of Netdraw.
Screenshot of Netdraw.

Figure A2

Screenshot of UCINET’s Interactive Dichotomization routine’s results.
Screenshot of UCINET’s Interactive Dichotomization routine’s results.

G-transitivity decomposition command line instruction and output in UCINET_

->dsp gtrans(women)
1 2 3 4
Level Trans Intrans Possible Prop Trans
n
-------- -------- -------- --------
7 0 0 0
6 26 0 26 1
5 30 0 30 1
4 160 0 160 1
3 526 4 530 0.992
2 2,032 44 2,076 0.979
1 3,786 292 4,078 0.928
0 4,448 448 4,896 0.908

One mode DGG Women by Women network projection_

EVLATHBRCHFRELPERUVEMYKASYNOHEDOOLFL
EVELYN867634333222221211
LAURA676634423211222100
THERESA768644434322332211
BRENDA666744423211222100
CHARLOTTE334442202100111000
FRANCES444424322111111100
ELEANOR344423423211222100
PEARL323202232222221211
RUTH334322324322322211
VERNE223211223433433211
MYRNA212101122344433211
KATHERINE212101122346653211
SYLVIA223211223446764211
NORA223211222335684122
HELEN122211212333445111
DOROTHY212101122222211211
OLIVIA101000011111121122
FLORA101000011111121122

R-square of models predicting performance using betweenness centrality at different levels of dichotomization_

Dichot. levelR2
10.05
20.09
30.12
40.23
50.31
60.27
70.22
80.15
90.07

Number of g-transitive and intransitive triples in the DGG dataset at different dichotomization levels_

ValueTransIntrans
700
6260
5300
41600
35264
22,03244
13,786292
04,448448

Z-score, correlation, number of ties and density of the DGG dataset at different dichotomization levels_

ValueZ-scoreCorrelationTiesDensity
73.3520.27188720.006536
62.6670.646625160.052288
51.9830.666829180.058824
41.2980.781314480.156863
30.6130.811928920.300654
2−0.0720.7201151900.620915
1−0.7560.4573412780.908497
0−1.441 3061.000000
DOI: https://doi.org/10.21307/connections-2018-002 | Journal eISSN: 2816-4245 | Journal ISSN: 0226-1766
Language: English
Page range: 1 - 11
Published on: Jan 18, 2019
Published by: International Network for Social Network Analysis (INSNA)
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year

© 2019 Stephen P. Borgatti, Eric Quintane, published by International Network for Social Network Analysis (INSNA)
This work is licensed under the Creative Commons Attribution 4.0 License.