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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["faculty_hiring/computer_science"]
Three networks of faculty hiring in Computer Science Departments, Business Schools, and History Departments. Each node is a PhD-granting institution in the respective field, and a directed edge (i,j) indicates that a person received their PhD from node i and was tenure-track faculty at node j during time of collection (2011-2013). All data collected from faculty public rosters at the sampled institutions1
Name | Nodes | Edges | $\left<k\right>$ | $\sigma_k$ | $\lambda_h$ | $\tau$ | $r$ | $c$ | $\oslash$ | $S$ | Kind | Mode | NPs | EPs | gt | GraphML | GML | csv |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
computer_science | 206 | 4,988 | 24.21 | 62.95 | 41.69 | 2.28 | -0.07 | 0.38 | 3 | 1.00 | Directed | Unipartite | name pi USN2010 NRC95 Region institution | rank gender | 14 KiB | 30 KiB | 27 KiB | 19 KiB |
business | 113 | 9,042 | 80.02 | 148.21 | 60.48 | 1.42 | -0.09 | 0.68 | 2 | 1.00 | Directed | Unipartite | name pi USN2012 NRC-- Region institution | rank gender | 13 KiB | 38 KiB | 35 KiB | 22 KiB |
history | 145 | 4,538 | 31.30 | 75.92 | 44.78 | 1.51 | -0.12 | 0.52 | 3 | 1.00 | Directed | Unipartite | name pi USN2009 NRC2010 Region institution | rank gender | 11 KiB | 26 KiB | 23 KiB | 16 KiB |