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Are We in Agreement? Benchmarking and Reliability Issues between Social Network Analytic Programs

Philip J. Murphy
Philip J. Murphy
, 
Karen T. Cuenco
Karen T. Cuenco
 oraz 
YuFei Wang
YuFei Wang
   | 04 cze 2018
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Data publikacji: 04 cze 2018
Zakres stron: 23 - 44
DOI: https://doi.org/10.21307/connections-2017-002
© 2017 Emmanuel Lazega et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1

Scatterplot matrix comparing closeness centrality output for a large, two-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal.
Scatterplot matrix comparing closeness centrality output for a large, two-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal.

Figure 3

A variety of solutions are possible when analyzing two-mode networks in UCINET. Top Row: Scatterplots of UCINET’s degree (r = 0.3713) and closeness (r = -0.0134) output using the two-mode centrality procedure, compared with other analytic packages. All other packages performed identically. Bottom Row: When transformed into a bipartite network format, UCINET calculates as for a one-mode network, and results are analogous to other packages. Closeness centrality for the bipartite aspect was calculated using Freeman normalization in UCINET.
A variety of solutions are possible when analyzing two-mode networks in UCINET. Top Row: Scatterplots of UCINET’s degree (r = 0.3713) and closeness (r = -0.0134) output using the two-mode centrality procedure, compared with other analytic packages. All other packages performed identically. Bottom Row: When transformed into a bipartite network format, UCINET calculates as for a one-mode network, and results are analogous to other packages. Closeness centrality for the bipartite aspect was calculated using Freeman normalization in UCINET.

Figure 2

Scatterplot matrix comparing output for closeness centrality in a small, one-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal. Note that the sna package for R does not produce measures between disconnected components, resulting in correlation values listed as “NA”.
Scatterplot matrix comparing output for closeness centrality in a small, one-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal. Note that the sna package for R does not produce measures between disconnected components, resulting in correlation values listed as “NA”.

Figure 4

Scatterplot matrix comparing degree centrality output for a small, one-mode network containing loops. Pearson’s correlation coefficients between programs are provided above the diagonal.
Scatterplot matrix comparing degree centrality output for a small, one-mode network containing loops. Pearson’s correlation coefficients between programs are provided above the diagonal.

Figure 5

Scatterplot matrix comparing eigenvector centrality output for a large, two-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal. Pajek output for this plot was calculated using “important vertices”, a two-mode generalization of hubs and authorities.
Scatterplot matrix comparing eigenvector centrality output for a large, two-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal. Pajek output for this plot was calculated using “important vertices”, a two-mode generalization of hubs and authorities.

Figure 6

Scatterplot matrix of eigenvector centrality output for a small, one-mode network with loops. Pearson’s correlation coefficients between programs are provided above the diagonal. Note, initial calculations in sna – shown above – were run using the default argument (diag=FALSE). For additional variation, consult the text above.
Scatterplot matrix of eigenvector centrality output for a small, one-mode network with loops. Pearson’s correlation coefficients between programs are provided above the diagonal. Note, initial calculations in sna – shown above – were run using the default argument (diag=FALSE). For additional variation, consult the text above.

Figure 7

Scatterplot matrix comparing eigenvector centrality output for a moderately large, one-mode network containing loops and disconnected components. Pearson’s correlation coefficients between programs are provided above the diagonal.
Scatterplot matrix comparing eigenvector centrality output for a moderately large, one-mode network containing loops and disconnected components. Pearson’s correlation coefficients between programs are provided above the diagonal.

Figure 8

Scatterplot matrix comparing betweenness centrality output for a large, one-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal.
Scatterplot matrix comparing betweenness centrality output for a large, one-mode network. Pearson’s correlation coefficients between programs are provided above the diagonal.

Figure 9

Scatterplot matrices comparing degree and eigenvector output for a two-mode network. Neither network contains loops or disconnected components. Pearson’s correlation coefficients between programs are provided above the diagonal.
Scatterplot matrices comparing degree and eigenvector output for a two-mode network. Neither network contains loops or disconnected components. Pearson’s correlation coefficients between programs are provided above the diagonal.

Analytic interfaces used in this study

Version Source
UCINET 6.564 http://www.analytictech.com/
Pajek 4.03 http://pajek.imfm.si/
ORA-NetScenes 3.0.9.9.20 http://www.casos.cs.cmu.edu/projects/ora/
Gephi 0.8.2 http://gephi.org/
sna 2.3-2 http://www.statnet.org/
igraph 0.7.1 http://igraph.org/

Data used for reliability comparisons

Data Nodes in Main Component Nodes in Smaller Components Number of Loops Max. Number of Nodes Average Degree
Small One-mode 29 6 6 35 3.5
Large One-mode 1876 112 60 2000 3.0
Small Two-mode (10, 21) 4 NA (10, 25) 3.7
Large Two-mode (300, 1815) 185 NA (300, 1700) 3.7

Output scaling

Degree Closeness Betweenness Eigenvector
Gephi Raw Normalized Average Raw Scaled (max=1)
Pajek Raw Normalized Normalized Normalized
UCINET Raw Average Raw Normalized
ORA Normalized Normalized Normalized Normalized
sna Raw Normalized Raw Normalized
igraph Raw Normalized Raw Scaled (max=1)

Consistency of output by centrality type and network conditions

No Disconnected Components & No Loops Disconnected Components Loops Disconnected Components Loops
1 Mode 2 Mode 1 Mode 2 Mode 1 Mode 2 Mode 1 Mode 2 Mode
Betweenness Centrality High High High High High NA High NA
Degree Centrality High Medium High Medium Low NA Low NA
Eigenvector Centrality Medium Medium Medium Low Medium NA Low NA
Closeness Centrality Medium Medium Low Low Medium NA Low NA
eISSN:
0226-1766
Język:
Angielski
Częstotliwość wydawania:
Volume Open
Dziedziny czasopisma:
Social Sciences, other
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