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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["physics_collab/pierreAuger"]
(and likewise for the other networks available.)

physics_collab — Multilayer physicist collaborations (2015)


Two multiplex networks of coauthorships among the Pierre Auger Collaboration of physicists (2010-2012) and among researchers who have posted preprints on arXiv.org (all papers up to May 2014). Layers represent different categories of publication, and an edge's weight indicates the number of reports written by the authors. These layers are one-mode projections from the underlying author-paper bipartite network1

  1. Description obtained from the ICON project. 

Social Collaboration Weighted Multilayer Projection
  • M. De Domenico et al., "Identifying modular flows on multilayer networks reveals highly overlapping organization in interconnected systems." Physical Review X 5(1), 011027 (2015)., https://arxiv.org/abs/1408.2925
Upstream URL OK
Upstream license
Open Data Commons Open Database License (ODbL), https://opendatacommons.org/licenses/odbl/
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
pierreAuger 514 7,153 27.83 29.42 75.46 64.13 0.65 0.86 9 0.92 Undirected Unipartite name weight layer 16 KiB 38 KiB 36 KiB 30 KiB
arXiv 14,488 59,026 8.15 13.67 47.00 918.69 0.19 0.35 18 0.61 Undirected Unipartite name weight layer 343 KiB 600 KiB 545 KiB 464 KiB
pierreAuger drawing
arXiv drawing