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You may also take a look at the source code.
The network in this dataset can be loaded directly from graph-tool with:
import graph_tool.all as gt
g = gt.collection.ns["notre_dame_web"]

notre_dame_web — Webgraph (Notre Dame)

Description

The web graph of Notre Dame University (nd.edu), as collected in 1999.1


  1. Description obtained from the ICON project. ↩

Tags
Informational Web graph Unweighted
Citation
Upstream URL OK
http://snap.stanford.edu/data/web-NotreDame.html
Networks
Tip: hover your mouse over a table header 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
notre_dame_web 325,729 1,497,134 4.60 48.38 177.36 89281.26 -0.04 0.23 46 1.00 Directed Unipartite 5.3 MiB 9.0 MiB 9.1 MiB 7.9 MiB
Ridiculograms*
None drawing
* 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.