facebook_organizations — Within-organization Facebook friendships (2012)
- Description
Six networks of Facebook friendships among employees of six anonymized high-tech companies, collected in 2012 by M. Fire and R. Puzis. Nodes are Facebook users who stated in their public profile that they worked for the target company, and an undirected edge connects two such users who are Facebook friends; only friendships between employees of the same company are included. The data were obtained with a focused crawler that starts from seed profiles of known employees and preferentially explores profiles with many friends already identified as employees, matching company-related keywords in each public profile. User identities were anonymized by replacing Facebook IDs with consecutive integers, which are stored in the vertex property
name.The companies are grouped by size: small (
S1,S2; 500 to 2,000 employees), medium (M1,M2; 4,000 to 20,000 employees) and large (L1,L2; more than 50,000 employees). Companies labeled 1 were crawled with the first version of the crawler, which stops when its queue is exhausted, and those labeled 2 with a second version with a stricter stopping condition. The companies described in the paper are a network hardware company (S1), a software company (S2), a telecommunication services company (M1), a software and outsourcing company (M2), and two large multinational technology corporations (L1, L2). Note that the sizes of the publishedS1(320 nodes, 2,369 edges) andS2(165 nodes, 726 edges) networks are swapped relative to those reported in the paper. The employee job positions analyzed in the paper are not included in the data.
- Tags
- Social Online Unweighted
- Citation
-
- M. Fire, and R. Puzis, "Organization mining using online social networks." Networks and Spatial Economics 16(2), 545-578 (2016) arXiv:1303.3741
- Upstream URL ☠
- The upstream source has since vanished. The files can be temporarily accessed here.
- Networks
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Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
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 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 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 127 KiB 130 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 198 KiB 349 KiB 370 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 160 KiB 255 KiB 262 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 244 KiB 412 KiB 430 KiB 353 KiB - Ridiculograms*