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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["word_adjacency/darwin"]
(and likewise for the other networks available.)

word_adjacency — Word Adjacency Networks

Description

Directed Networks of word adjacency in texts of several languages including English, French, Spanish and Japanese1


  1. Description obtained from the ICON project. ↩

Tags
Informational Language Unweighted
Citation
Upstream URL OK
https://doi.org/10.1126/science.1089167
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
darwin 7,381 46,281 6.27 66.54 104.34 7.54 -0.24 0.04 8 1.00 Directed Unipartite name 192 KiB 312 KiB 298 KiB 235 KiB
french 8,325 24,295 2.92 36.54 52.46 13.38 -0.23 0.01 9 1.00 Directed Unipartite name 178 KiB 267 KiB 241 KiB 197 KiB
spanish 11,586 45,129 3.90 63.30 93.51 20.78 -0.28 0.02 10 1.00 Directed Unipartite name 250 KiB 400 KiB 365 KiB 290 KiB
japanese 2,704 8,300 3.07 26.84 38.16 7.39 -0.26 0.03 8 1.00 Directed Unipartite name 59 KiB 87 KiB 75 KiB 63 KiB
Ridiculograms*
darwin drawing
darwin
french drawing
french
spanish drawing
spanish
japanese drawing
japanese
* These are automatically generated force-directed visualizations, and can be quite meaningless for networks both big and small. They should not be taken seriously as sources of scientific insight. See here for a discussion.