Neil Zhenqiang Gong
Computer Science Division
University of California Berkeley
Office: 721 Soda Hall
Google+ Social Networks with Node Attributes
This published dataset consisting of 4 Google+ snapshots is a subset of the dataset studied in our IMC'12 paper. Each snapshot includes both directed social structure and node attributes, which can be represented by the following Social-Attribute Network. Snapshots 3 and 4 were crawled after Google+ was opened to the public.
Table I. Dataset summary
Directed social structure
UserIDFrom UserIDTo TimeID
Each line corresponds to a directed link. UserIDs are anonimyzed to be integers starting from 0. TimeID is 0, 1, 2 or 3, indicating the snapshot in which this directed link first appears.
UserID AttriID TimeID
Each line corresponds to an undirected attribute link. AttriID are anonimyzed to be negative integers starting from -1. Again, TimeID is 0, 1, 2 or 3, indicating the snapshot in which this link first appears.
Each line corresponds to an attribute. AttriType could be employer, school, major or places_lived.
Reconstructing the tth Snapshot
To obtain the tth snapshot, you should keep all edges whose TimeIDs are less than t, where t=1,2,3,4.
- Evolution of Social-Attribute Networks: Measurements, Modeling, and Implications using Google+
Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, Prateek Mittal, Emil Stefanov, Vyas Sekar, Dawn Song
ACM/USENIX Internet Measurement Conference (IMC), 2012
- Jointly Predicting Links and Inferring Attributes using a Social-Attribute Network (SAN)
Neil Zhenqiang Gong, Ameet Talwalkar, Lester Mackey, Ling Huang, Richard Shin, Emil Stefanov, Elaine Shi, Dawn Song
ACM Workshop on Social Network Mining and Analysis (SNA-KDD), 2012
Dataset Release Policy
- To download the dataset, please send emails to Neil Zhenqiang Gong (email@example.com) with "[G+ Request]" in the subject. We will tell you the links to download the dataset. In your email, please include the following information (if we
don't know each other).
The information is needed for verification
- A short description about what you're going to do with our dataset. Some keywords (e.g., link prediction, attribute inference, evolution) are enough. We don't need to know details.
- If your papers use our dataset, please cite our papers.
- You're not allowed to further distribute the dataset without our permission.
Sending us emails for our dataset implies that you are aware of and agree with the above policies.