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You may also take a look at the source code.
The networks in this dataset can be loaded directly from graph-tool with:(and likewise for the other networks available.)import graph_tool.all as gt g = gt.collection.ns["facebook_organizations/S1"]
Six networks of friendships among users on Facebook who indicated employment at one of the target corporation. Companies range in size from small to large. Only edges between employees at the same company are included in a given snapshot.1
Name | Nodes | Edges | $\left<k\right>$ | $\sigma_k$ | $\lambda_h$ | $\tau$ | $r$ | $c$ | $\oslash$ | $S$ | Kind | Mode | NPs | EPs | gt | GraphML | GML | csv |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
S1 | 320 | 2,369 | 14.81 | 14.26 | 28.22 | 6.83 | -0.02 | 0.29 | 7 | 1.00 | Undirected | Unipartite | name | 9 KiB | 14 KiB | 13 KiB | 11 KiB | |
S2 | 165 | 726 | 8.80 | 8.33 | 15.24 | 8.79 | -0.07 | 0.33 | 6 | 1.00 | Undirected | Unipartite | name | 4 KiB | 6 KiB | 5 KiB | 5 KiB | |
M1 | 1,429 | 32,876 | 46.01 | 51.31 | 66.88 | 26.81 | 0.09 | 0.26 | 7 | 1.00 | Undirected | Unipartite | name | 67 KiB | 152 KiB | 137 KiB | 103 KiB | |
M2 | 3,862 | 87,324 | 45.22 | 29.58 | 70.32 | 4.80 | 0.09 | 0.23 | 5 | 1.00 | Undirected | Unipartite | name | 197 KiB | 423 KiB | 386 KiB | 304 KiB | |
L1 | 5,793 | 45,266 | 15.63 | 30.36 | 56.27 | 2931.09 | 0.18 | 0.31 | 16 | 1.00 | Undirected | Unipartite | name | 159 KiB | 290 KiB | 273 KiB | 208 KiB | |
L2 | 5,524 | 94,219 | 34.11 | 31.81 | 76.53 | 207.70 | 0.10 | 0.22 | 9 | 1.00 | Undirected | Unipartite | name | 243 KiB | 486 KiB | 445 KiB | 353 KiB |