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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["berkstan_web"]

berkstan_web — Webgraph (Berkeley-Stanford)


The web graph of Berkeley and Stanford Universities (berkeley.edu and stanford.edu), as collected in 2002. Nodes represent pages and directed edges represent hyperlinks between them.

Informational Web graph Unweighted
  • J. Leskovec, K. Lang, A. Dasgupta, M. Mahoney. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters. Internet Mathematics 6(1) 29--123, 2009, https://arxiv.org/abs/0810.1355
Upstream URL OK
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
berkstan_web 685,231 7,600,595 11.09 285.98 663.80 389618.19 -0.10 0.01 208 0.96 Directed Unipartite 11.9 MiB 29.9 MiB 28.6 MiB 27.2 MiB
None drawing