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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:
import graph_tool.all as gt
g = gt.collection.ns["facebook_organizations/S1"]
(and likewise for the other networks available.)

facebook_organizations — Within-organization Facebook friendships (2013)

Description

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. Node metadata gives listed job-type on the user's page.1


  1. Description obtained from the ICON project. 

Tags
Social Online Unweighted Metadata
Citation
  • M. Fire, and R. Puzis, "Organization mining using online social networks." Networks and Spatial Economics 16(2), 545-578 (2016), https://arxiv.org/abs/1303.3741
Upstream URL [Errno 110] Connection timed out
https://homes.cs.washington.edu/~fire/#section3
Networks
Tip: click on the table header to sort the list. Hover your mouse over it to obtain a legend.
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 15 KiB 14 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 6 KiB 4 KiB
M1 1,429 32,876 46.01 51.31 66.88 26.81 0.09 0.26 7 1.00 Undirected Unipartite name 69 KiB 139 KiB 146 KiB 104 KiB
M2 3,862 87,324 45.22 29.58 70.32 4.80 0.09 0.23 5 1.00 Undirected Unipartite name 201 KiB 380 KiB 379 KiB 304 KiB
L1 5,793 45,266 15.63 30.36 56.27 2931.02 0.18 0.31 16 1.00 Undirected Unipartite name 157 KiB 264 KiB 251 KiB 206 KiB
L2 5,524 94,219 34.11 31.81 76.53 207.70 0.10 0.22 9 1.00 Undirected Unipartite name 236 KiB 429 KiB 429 KiB 353 KiB
Ridiculograms
S1 drawing
S1
S2 drawing
S2
M1 drawing
M1
M2 drawing
M2
L1 drawing
L1
L2 drawing
L2