flickr_growth — Flickr friendship network (2008)
- Description
Growth of the network of contacts among users of the photo sharing site Flickr, collected by A. Mislove and collaborators. Flickr users can add other users as contacts, which does not require the consent of the latter. The authors crawled the network once per day with automated scripts, using the Flickr API and following forward links from the users already discovered, between 2 November and 3 December 2006 and between 3 February and 18 May 2007 (104 days in total), and recorded the day on which each link was first observed. Since Flickr does not provide the creation times of the links, the times have a resolution of one day, and links that existed when the crawl started have the date of the first crawl (2 November 2006). New users could only be discovered once they became connected to an already crawled user.
Nodes are users and a directed edge $(i,j)$ indicates that user $i$ added user $j$ as a contact. The network contains a single self-loop. The user IDs were anonymized.
The network has the following properties:
- (Node)
user_id: the anonymized user ID in the source data. - (Edge)
time: the day on which the link was first observed, as the Unix time of 00:00 UTC of that day.
- (Node)
- Tags
- Social Online Unweighted Timestamps
- Added
- 2020-08-11
- Citation
-
- A. Mislove, H. S. Koppula, K. P. Gummadi, P. Druschel, B. Bhattacharjee, "Growth of the Flickr social network." Proceedings of the 1st Workshop on Online Social Networks (WOSN '08), 25-30 (2008) 10.1145/1397735.1397742 Sci-Hub
- Upstream URL OK
- http://socialnetworks.mpi-sws.org/data-wosn2008.html
- Networks
-
Tip: hover over a column header 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
- Ridiculograms*
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