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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["faculty_hiring/computer_science"]
(and likewise for the other networks available.)

faculty_hiring — Faculty hiring networks (Comp. Sci., Business, History)


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

  1. Description obtained from the ICON project. 

Economic Employment Weighted Metadata
Upstream URL OK
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
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
computer_science drawing
business drawing
history drawing