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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["bookcrossing"]

bookcrossing — BookCrossing ratings (2005)


Two bipartite networks representing people and the books they have interacted with, from the BookCrossing website. Nodes represent users and books, and an edge connects a user to a book they have interacted with. The file book_implicit is unweighted; edge weights in book_ratings give the rating a user assigned to a book.1

  1. Description obtained from the ICON project. 

Economic Preferences Unweighted Weighted
Upstream URL [Errno 113] No route to host
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
bookcrossing 445,801 1,149,739 5.16 45.45 134.05 187.56 -0.08 0.00 19 0.94 Undirected Bipartite 9.1 MiB 13.1 MiB 12.8 MiB 11.8 MiB
None drawing