foursquare_nyc_tokyo — Foursquare check-ins in New York and Tokyo (2012-2013)

Description

Two bipartite networks of users and venues of the location-based social network Foursquare, from check-ins in New York City (NYC) and Tokyo (TKY) collected by D. Yang and collaborators between 12 April 2012 and 16 February 2013 (about ten months). Nodes are users and venues, and an undirected edge is a check-in of a user at a venue, annotated with its time and GPS coordinates; repeated check-ins of the same user at the same venue appear as parallel edges. The NYC network contains 227,428 check-ins and the TKY network 573,703 check-ins. The data were originally used to study the spatio-temporal regularity of user activities.

The networks have the following properties:

Nodes: is_user, whether the node is a user (otherwise it is a venue); name, the anonymized user ID or the Foursquare venue ID; category and category_id, the name and Foursquare ID of the fine-grained venue category (e.g. "Bridge", "Home (private)"; empty for users). A few hundred venues have check-ins with different categories; for these, the category of their first check-in in the file is used.
Edges: time, the time of the check-in (Unix time, UTC); tz_offset, the offset of the local time with respect to UTC, in minutes; latitude and longitude, the GPS coordinates of the check-in (which may vary slightly between check-ins at the same venue).¹

Description obtained in part from the ICON project. ↩

Tags

Social Online Unweighted Metadata Timestamps Multigraph Spatial

Citation

D. Yang, D. Zhang, V. W. Zheng, Z. Yu, "Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs." IEEE Transactions on Systems, Man, and Cybernetics: Systems 45(1), 129-142 (2015) 10.1109/TSMC.2014.2327053 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	39,416	227,428	11.54	48.52	36.66	79.13	-0.18	0.00	8	1.00	Undirected	Bipartite	is_user name category category_id	time tz_offset latitude longitude	2.6 MiB	3.2 MiB	3.1 MiB	3.9 MiB
TKY	64,151	573,703	17.89	111.42	96.53	35.91	-0.02	0.00	8	1.00	Undirected	Bipartite	is_user name category category_id	time tz_offset latitude longitude	5.5 MiB	7.1 MiB	6.8 MiB	8.8 MiB