foursquare — Foursquare NYC restaurants (2012)
- Description
Two bipartite networks of users and restaurant venues in New York City on the location-based social network Foursquare, collected by D. Yang and collaborators. Nodes are users and venues, and user and venue identifiers are anonymized but consistent across the two networks.
NYC_restaurant_checkin: each edge is a check-in of a user at a restaurant, between 24 October 2011 and 20 February 2012; repeated check-ins of the same user at the same venue appear as parallel edges.NYC_restaurant_tips: each edge is a tip (a short text comment) left by a user on a restaurant. Tips were collected without time limit, so a user may have left a tip on a venue without checking in there during the check-in collection period.
The networks have the following properties:
- (Node)
is_user: whether the node is a user (True) or a venue (False). - (Node)
name: the anonymized user or venue ID. - (Node)
tags: for venues, the list of tags that users added to the venue (e.g. "cupcake", "trendy"); empty for users and for venues without tags. - (Edge)
tip_text(edges,NYC_restaurant_tipsonly): the text of the tip.
- Tags
- Social Online Unweighted Metadata
- Added
- 2020-08-11
- Citation
-
- 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). 10.1145/2493432.2493464 Sci-Hub
- Upstream URL OK
- https://sites.google.com/site/yangdingqi/home/foursquare-dataset
- Networks
-
Tip: click on a column header to sort the list, or hover over it for a description.
Column legend
- $\left<k\right>$
- Mean degree
- $\sigma_k$
- Standard deviation of the degrees
- $\lambda_h$
- Largest eigenvalue of the Hashimoto (non-backtracking) matrix
- $\tau$
- Random walk mixing time
- $r$
- Degree assortativity
- $c$
- Global clustering coefficient
- $\oslash$
- (Pseudo-) diameter
- $S$
- Relative size of the largest component
- NPs
- Node properties
- EPs
- Edge properties
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 171 KiB 246 KiB 242 KiB 197 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 468 KiB 559 KiB 550 KiB 563 KiB - Ridiculograms*