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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:(and likewise for the other networks available.)import graph_tool.all as gt g = gt.collection.ns["physics_collab/pierreAuger"]
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
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 | 35 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 | 546 KiB | 464 KiB |