Calculates some indicators for each node in the network.
References
Jung, N., Bertrand, F., Bahram, S., Vallat, L., and Maumy-Bertrand, M. (2014). Cascade: a R-package to study, predict and simulate the diffusion of a signal through a temporal gene network. Bioinformatics, btt705.
Vallat, L., Kemper, C. A., Jung, N., Maumy-Bertrand, M., Bertrand, F., Meyer, N., ... & Bahram, S. (2013). Reverse-engineering the genetic circuitry of a cancer cell with predicted intervention in chronic lymphocytic leukemia. Proceedings of the National Academy of Sciences, 110(2), 459-464.
Examples
data(network)
analyze_network(network,nv=0)
#> Loading required package: tnet
#> Loading required package: igraph
#>
#> Attaching package: ‘igraph’
#> The following object is masked from ‘package:Cascade’:
#>
#> compare
#> The following objects are masked from ‘package:stats’:
#>
#> decompose, spectrum
#> The following object is masked from ‘package:base’:
#>
#> union
#> Loading required package: survival
#> tnet: Analysis of Weighted, Two-mode, and Longitudinal networks.
#> Type ?tnet for help.
#> node betweenness degree output closeness
#> 1 1 0 52 2.493277168 110.83148964
#> 2 2 0 51 1.873353046 91.33091185
#> 3 3 0 48 0.869999868 75.92040114
#> 4 4 0 44 1.712495042 82.94726787
#> 5 5 0 44 0.811053103 34.64847877
#> 6 6 0 51 5.326562329 152.65540461
#> 7 7 0 50 3.448573704 104.41478218
#> 8 8 0 49 1.086343159 32.84110423
#> 9 9 0 52 0.781594709 35.17592984
#> 10 10 23 47 2.193224490 59.86437756
#> 11 11 156 48 3.844765796 92.87430047
#> 12 12 57 35 1.356068126 38.80420637
#> 13 13 171 44 5.060737204 125.32045602
#> 14 14 7 43 0.991895840 31.47338043
#> 15 15 0 5 0.125014915 2.54460894
#> 16 16 1 9 0.073129838 1.48851710
#> 17 17 51 13 0.862046510 17.54647642
#> 18 18 18 9 0.365414532 7.43780922
#> 19 19 0 4 0.058039485 1.18136139
#> 20 20 4 9 0.299858554 6.10345380
#> 21 21 4 8 0.220877759 4.49584372
#> 22 22 20 13 0.370169895 7.53460197
#> 23 23 3 4 0.121253333 2.46804404
#> 24 24 5 9 0.238911191 4.86290418
#> 25 25 0 0 0.000000000 0.00000000
#> 26 26 4 12 0.200293631 4.07686527
#> 27 27 15 15 0.676393027 13.76760322
#> 28 28 20 7 0.386360480 7.86415231
#> 29 29 5 2 0.099861827 2.03263185
#> 30 30 104 18 1.831171079 37.27246708
#> 31 31 2 5 0.089213272 1.81588645
#> 32 32 13 13 0.311496789 6.34034358
#> 33 33 0 18 0.152389258 3.10179844
#> 34 34 15 13 0.580194498 11.80953575
#> 35 35 0 0 0.000000000 0.00000000
#> 36 36 0 0 0.000000000 0.00000000
#> 37 37 0 0 0.000000000 0.00000000
#> 38 38 0 0 0.000000000 0.00000000
#> 39 39 0 0 0.000000000 0.00000000
#> 40 40 0 0 0.000000000 0.00000000
#> 41 41 0 0 0.000000000 0.00000000
#> 42 42 0 0 0.000000000 0.00000000
#> 43 43 0 0 0.000000000 0.00000000
#> 44 44 0 0 0.000000000 0.00000000
#> 45 45 0 0 0.000000000 0.00000000
#> 46 46 0 0 0.000000000 0.00000000
#> 47 47 0 0 0.000000000 0.00000000
#> 48 48 0 0 0.000000000 0.00000000
#> 49 49 0 0 0.000000000 0.00000000
#> 50 50 0 0 0.000000000 0.00000000
#> 51 51 0 0 0.000000000 0.00000000
#> 52 52 0 0 0.000000000 0.00000000
#> 53 53 0 0 0.000000000 0.00000000
#> 54 54 0 0 0.000000000 0.00000000
#> 55 55 0 40 3.884171706 143.68410789
#> 56 56 28 30 1.213962675 34.79491085
#> 57 57 17 8 0.381344058 7.76204583
#> 58 58 6 15 0.839509022 23.76967274
#> 59 59 2 7 0.191033515 3.88838076
#> 60 60 0 22 0.590567211 73.18774872
#> 61 61 0 25 1.624415450 82.84061811
#> 62 62 0 9 0.086465686 1.75996086
#> 63 63 0 1 0.001638944 0.03335979
#> 64 64 7 26 1.344245113 37.33582438
#> 65 65 0 13 1.368119564 95.96946550
#> 66 66 25 9 0.505402921 10.28719487
#> 67 67 24 9 0.756855631 15.40537469
#> 68 68 0 11 0.175883203 12.74214052
#> 69 69 1 2 0.074150206 1.50928614
#> 70 70 10 34 1.050511028 32.75663861
#> 71 71 70 35 3.333568905 86.26612955
#> 72 72 0 8 0.211764898 4.31035651
#> 73 73 7 6 0.288167148 5.86548174
#> 74 74 11 10 0.352721778 7.17945528