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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["foursquare/NYC_restaurant_checkin"]
(and likewise for the other networks available.)

foursquare — Foursquare NYC restaurants (2012)


Two bipartite networks of users and restaurant locations in New York City on Foursquare, from 24 October 2011 to 20 February 2012. In one network, an edge denotes a check-in event of a user at a restaurant. In the other, an edge exists if a user left a tip/comment on a restaurant. Metadata include comments.1

  1. Description obtained from the ICON project. 

Social Online Unweighted Metadata
  • D. Yang, et al. "Fine-grained preference-aware location search leveraging crowdsourced digital footprints from LBSNs." Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing. ACM. (2013)., https://doi.org/10.1145/2493432.2493464 [@sci-hub]
Upstream URL [Errno -2] Name or service not known
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
NYC_restaurant_checkin 4,936 27,149 11.00 14.61 10.57 40.65 0.30 0.00 13 0.99 Undirected Bipartite is_user name tags 142 KiB 222 KiB 215 KiB 159 KiB
NYC_restaurant_tips 6,410 10,377 3.24 5.67 9.47 92.11 -0.04 0.00 19 0.84 Undirected Bipartite is_user name tags tip_text 439 KiB 525 KiB 519 KiB 521 KiB
NYC_restaurant_checkin drawing
NYC_restaurant_tips drawing