tSnaStats: Apply the sna package's static graph- and vertex-level...
In tsna: Tools for Temporal Social Network Analysis
Description
Usage
Arguments
Details
Value
Note
Author(s)
References
See Also
Examples
Description
Samples collapsed static networks at regular intervals along a network dynamic object, applies the named static sna descriptive statistic function to each network, and returns the result as a time series. Additional arguments to the function can be included via ... . Set the sna function's directedness and self-loops flags appropriately by default.
Usage
1
Arguments
nd
a networkDynamic object to be evaluated
snafun
character string giving the name of the sna package function to be applied. i.e 'mutuality'
start
optional numeric time value at which evaluation should start (default is first observed time)
end
optional numeric time value at which evaluation should end (default is last observed time)
time.interval
optional numeric value giving time interval between evaluations (default is 1)
aggregate.dur
optional numeric value giving the duration of time bin to aggregate over for each evaluation (default 0). See network.collapse
rule
character vector describing rule to be used if multiple attribute values are encountred when using non-zero aggregate.dur. Default is latest. See network.collapse for details
...
additional arguments to be passed on to the sna function. See docs for each function for possible arguments.
Details
This wrapper directly calls functions in the sna package, so it will only work if that package is installed. Below is a list of supported functions:
Graph-Level statistics:
-
components Number of (Maximal) Components Within a Given Graph
-
triad.census Davis and Leinhardt Triad Census
-
connectedness Graph Connectedness Scores
-
dyad.census Holland and Leinhardt MAN Dyad Census
-
efficiency Graph Efficiency Scores
-
gden Graph Density
-
grecip Graph Reciprocity
-
gtrans Graph Transitivity
-
hierarchy Graph Hierarchy Scores
-
lubness Graph LUBness Scores
-
mutuality Graph Mutuality
-
centralization Graph Centralization (must provide centrality measure)
Vertex-level statistics:
-
closeness Vertex Closeness Centrality Scores
-
betweenness Vertex Betweenness Centrality Scores
-
bonpow Vertex Bonacich Power Centrality Scores
-
degree Vertex Degree Centrality Scores
-
evcent Vertex Eigenvector Centrality Scores
-
flowbet Vertex Flow Betweenness Scores
-
graphcent Vertex (Harary) Graph Centrality Scores
-
infocent Vertex Information Centrality Scores
-
loadcent Vertex Load Centrality Scores
-
prestige Vertex Prestige Scores
Most of the sna functions involve converting the network to a matrix and can be quite expensive to calculate for a single time point, so use care when applying to large or long-duration networks.
Some of the sna functions are undefined or produce numerical errors when applied to networks with certain configurations (such as zero edges).
The sna functions generally cannot handle networks with no vertices, so stats will be replaced with NA when they are encountered.
There may be some overlap with ergm terms available through tErgmStats and the ergm version will generally be faster
Value
a ts (time series) object. A matrix in which rows correspond to the time points evaluated and columns correspond to values of statistics produced. In the case of vertex-level indices, there will be one column per vertex. For the census measures, each column will correspond to a census element.
Note
Note that this is an early DRAFT implementation. Does not yet include binning options needed for non-discrete time networks, and has not been tested with networks that have changing vertex activity.
Author(s)
skyebend@uw.edu
References
Carter T. Butts (2014). sna: Tools for Social Network Analysis. R package version
2.3-2. http://CRAN.R-project.org/package=sna
See Also
See also tErgmStats,
Examples
1
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library(networkDynamicData)
data(harry_potter_support)
# compute triad census scores for each time point
tSnaStats(harry_potter_support,snafun='triad.census')
# compute graph transitivities
tSnaStats(harry_potter_support,snafun='gtrans')
## Not run:
data(concurrencyComparisonNets)
# since thes are big nets, with lots of timepoints,
# set time.interval to avoid evaluating every step
tSnaStats(base,'prestige',time.interval=25,rescale=TRUE)
# since it is time series, easy to plot
plot(tSnaStats(base,'components',time.interval=10))
## End(Not run)
Example output
Loading required package: network
network: Classes for Relational Data
Version 1.16.1 created on 2020-10-06.
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.
Loading required package: networkDynamic
networkDynamic: version 0.10.1, created on 2020-01-16
Copyright (c) 2020, 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").
Time Series:
Start = 0
End = 6
Frequency = 1
003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
0 40488 222 911 0 2 0 20 0 0 0 0 0 1 0 0 20
1 38582 942 1901 1 32 6 66 8 0 0 37 6 18 4 28 33
2 38726 879 1850 0 20 2 54 20 1 0 36 3 15 1 16 41
3 40378 556 668 0 5 5 21 11 0 0 4 1 4 1 0 10
4 37581 594 3145 0 22 8 106 16 0 0 93 2 7 0 12 78
5 39638 939 969 2 2 10 23 33 3 0 22 0 9 0 4 10
6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Time Series:
Start = 0
End = 6
Frequency = 1
Series 1
[1,] 0.8591549
[2,] 0.6374046
[3,] 0.6666667
[4,] 0.6016949
[5,] 0.6141176
[6,] 0.4492754
[7,] NA
Time Series:
Start = 0
End = 100
Frequency = 0.04
1 2 3 4 5 6
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25 0.000000000 0.001373626 0.001373626 0.004120879 0.002747253 0.000000000
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89 90 91 92 93 94 95
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181 182 183 184 185 186
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187 188 189 190 191 192 193
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194 195 196 197 198 199
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200 201 202 203 204 205 206
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207 208 209 210 211 212
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213 214 215 216 217 218
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219 220 221 222 223 224
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225 226 227 228 229 230 231
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75 0.000000000 0.001201923 0.001201923 0 0.001201923 0 0.001201923
100 0.000000000 0.000000000 0.002688172 0 0.001344086 0 0.000000000
232 233 234 235 236 237
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100 0.000000000 0.001344086 0.001344086 0.000000000 0.001344086 0.000000000
238 239 240 241 242 243
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244 245 246 247 248 249 250
0 0.000000000 0.001347709 0.002695418 0.000000000 0 0.002695418 0.001347709
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414 415 416 417 418 419
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100 0.000000000 0.002688172 0.000000000 0 0.001344086 0.004032258 0.001344086
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922 923 924 925 926 927 928
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929 930 931 932 933 934 935
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936 937 938 939 940 941
0 0.001347709 0.000000000 0.002695418 0.002695418 0.001347709 0.000000000
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942 943 944 945 946 947 948
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100 0.001344086 0.001344086 0.001344086 0.001344086 0 0.000000000 0.001344086
949 950 951 952 953 954 955
0 0.001347709 0.001347709 0 0.000000000 0.002695418 0.001347709 0.002695418
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956 957 958 959 960 961
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962 963 964 965 966 967
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968 969 970 971 972 973
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974 975 976 977 978 979
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100 0.001344086 0.002688172 0.001344086 0.001344086 0.000000000 0.001344086
980 981 982 983 984 985
0 0.001347709 0.002695418 0.002695418 0.001347709 0.001347709 0.000000000
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986 987 988 989 990 991
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992 993 994 995 996 997 998
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999 1000
0 0.001347709 0.000000000
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50 0.000000000 0.001358696
75 0.001201923 0.000000000
100 0.004032258 0.000000000
tsna documentation built on Nov. 1, 2021, 5:06 p.m.
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Description Usage Arguments Details Value Note Author(s) References See Also Examples
Description
Samples collapsed static networks at regular intervals along a network dynamic object, applies the named static sna descriptive statistic function to each network, and returns the result as a time series. Additional arguments to the function can be included via ... . Set the sna function's directedness and self-loops flags appropriately by default.
Usage
1 |
Arguments
nd |
a |
snafun |
character string giving the name of the |
start |
optional numeric time value at which evaluation should start (default is first observed time) |
end |
optional numeric time value at which evaluation should end (default is last observed time) |
time.interval |
optional numeric value giving time interval between evaluations (default is 1) |
aggregate.dur |
optional numeric value giving the duration of time bin to aggregate over for each evaluation (default 0). See |
rule |
character vector describing rule to be used if multiple attribute values are encountred when using non-zero |
... |
additional arguments to be passed on to the sna function. See docs for each function for possible arguments. |
Details
This wrapper directly calls functions in the sna package, so it will only work if that package is installed. Below is a list of supported functions:
Graph-Level statistics:
-
componentsNumber of (Maximal) Components Within a Given Graph -
triad.censusDavis and Leinhardt Triad Census -
connectednessGraph Connectedness Scores -
dyad.censusHolland and Leinhardt MAN Dyad Census -
efficiencyGraph Efficiency Scores -
gdenGraph Density -
grecipGraph Reciprocity -
gtransGraph Transitivity -
hierarchyGraph Hierarchy Scores -
lubnessGraph LUBness Scores -
mutualityGraph Mutuality -
centralizationGraph Centralization (must provide centrality measure)
Vertex-level statistics:
-
closenessVertex Closeness Centrality Scores -
betweennessVertex Betweenness Centrality Scores -
bonpowVertex Bonacich Power Centrality Scores -
degreeVertex Degree Centrality Scores -
evcentVertex Eigenvector Centrality Scores -
flowbetVertex Flow Betweenness Scores -
graphcentVertex (Harary) Graph Centrality Scores -
infocentVertex Information Centrality Scores -
loadcentVertex Load Centrality Scores -
prestigeVertex Prestige Scores
Most of the sna functions involve converting the network to a matrix and can be quite expensive to calculate for a single time point, so use care when applying to large or long-duration networks.
Some of the sna functions are undefined or produce numerical errors when applied to networks with certain configurations (such as zero edges).
The sna functions generally cannot handle networks with no vertices, so stats will be replaced with NA when they are encountered.
There may be some overlap with ergm terms available through tErgmStats and the ergm version will generally be faster
Value
a ts (time series) object. A matrix in which rows correspond to the time points evaluated and columns correspond to values of statistics produced. In the case of vertex-level indices, there will be one column per vertex. For the census measures, each column will correspond to a census element.
Note
Note that this is an early DRAFT implementation. Does not yet include binning options needed for non-discrete time networks, and has not been tested with networks that have changing vertex activity.
Author(s)
skyebend@uw.edu
References
Carter T. Butts (2014). sna: Tools for Social Network Analysis. R package version 2.3-2. http://CRAN.R-project.org/package=sna
See Also
See also tErgmStats,
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | library(networkDynamicData)
data(harry_potter_support)
# compute triad census scores for each time point
tSnaStats(harry_potter_support,snafun='triad.census')
# compute graph transitivities
tSnaStats(harry_potter_support,snafun='gtrans')
## Not run:
data(concurrencyComparisonNets)
# since thes are big nets, with lots of timepoints,
# set time.interval to avoid evaluating every step
tSnaStats(base,'prestige',time.interval=25,rescale=TRUE)
# since it is time series, easy to plot
plot(tSnaStats(base,'components',time.interval=10))
## End(Not run)
|
Example output
Loading required package: network
network: Classes for Relational Data
Version 1.16.1 created on 2020-10-06.
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.
Loading required package: networkDynamic
networkDynamic: version 0.10.1, created on 2020-01-16
Copyright (c) 2020, 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").
Time Series:
Start = 0
End = 6
Frequency = 1
003 012 102 021D 021U 021C 111D 111U 030T 030C 201 120D 120U 120C 210 300
0 40488 222 911 0 2 0 20 0 0 0 0 0 1 0 0 20
1 38582 942 1901 1 32 6 66 8 0 0 37 6 18 4 28 33
2 38726 879 1850 0 20 2 54 20 1 0 36 3 15 1 16 41
3 40378 556 668 0 5 5 21 11 0 0 4 1 4 1 0 10
4 37581 594 3145 0 22 8 106 16 0 0 93 2 7 0 12 78
5 39638 939 969 2 2 10 23 33 3 0 22 0 9 0 4 10
6 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
Time Series:
Start = 0
End = 6
Frequency = 1
Series 1
[1,] 0.8591549
[2,] 0.6374046
[3,] 0.6666667
[4,] 0.6016949
[5,] 0.6141176
[6,] 0.4492754
[7,] NA
Time Series:
Start = 0
End = 100
Frequency = 0.04
1 2 3 4 5 6
0 0.000000000 0.000000000 0.000000000 0.001347709 0.000000000 0.000000000
25 0.000000000 0.001373626 0.001373626 0.004120879 0.002747253 0.000000000
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75 0.001201923 0.001201923 0.001201923 0.002403846 0.000000000 0.000000000
100 0.000000000 0.001344086 0.000000000 0.004032258 0.000000000 0.002688172
7 8 9 10 11 12
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13 14 15 16 17 18
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25 26 27 28 29 30
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100 0.000000000 0.000000000 0.000000000 0.004032258 0.000000000 0.001344086
31 32 33 34 35 36 37
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38 39 40 41 42 43 44 45 46
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100 0.001344086 0.002688172 0.002688172 0 0 0 0 0.000000000 0.002688172
47 48 49 50 51 52
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53 54 55 56 57 58 59
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100 0.001344086 0.000000000 0.002688172 0.000000000 0.001344086 0.000000000 0
60 61 62 63 64 65 66
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100 0.000000000 0.001344086 0.000000000 0.001344086 0.001344086 0 0.000000000
67 68 69 70 71 72 73 74 75
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76 77 78 79 80 81
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82 83 84 85 86 87 88
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100 0.001344086 0.001344086 0.001344086 0.002688172 0.001344086 0.001344086 0
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156 157 158 159 160 161
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207 208 209 210 211 212
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213 214 215 216 217 218
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219 220 221 222 223 224
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225 226 227 228 229 230 231
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244 245 246 247 248 249 250
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251 252 253 254 255 256 257
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258 259 260 261 262 263
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270 271 272 273 274 275
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276 277 278 279 280 281
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288 289 290 291 292 293
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306 307 308 309 310 311
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312 313 314 315 316 317 318
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319 320 321 322 323 324 325
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344 345 346 347 348 349 350
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358 359 360 361 362 363
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389 390 391 392 393 394
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922 923 924 925 926 927 928
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986 987 988 989 990 991
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0 0.000000000 0.001347709 0.000000000 0.000000000 0 0.000000000 0.001347709
25 0.000000000 0.002747253 0.000000000 0.000000000 0 0.000000000 0.000000000
50 0.000000000 0.001358696 0.000000000 0.001358696 0 0.000000000 0.000000000
75 0.000000000 0.001201923 0.000000000 0.001201923 0 0.000000000 0.000000000
100 0.001344086 0.000000000 0.001344086 0.002688172 0 0.001344086 0.000000000
999 1000
0 0.001347709 0.000000000
25 0.001373626 0.000000000
50 0.000000000 0.001358696
75 0.001201923 0.000000000
100 0.004032258 0.000000000
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