Zip-OSN: Towards Building Time Capsule for Online Social Activities
Online social networking sites such as Facebook, LinkedIn, and Twitter allow users to seek out friends or colleagues and interact with them in novel ways. At the same time, user-generated content has mushroomed in a collaborative manner in multiple contexts, e.g., online reviews of services/products, Wikipedia, and open software development. This new ecosystem of content generation, sharing, consumption, and innovation has completely reshaped the web by making it more of a massive online social system. This has huge implications on information propagation, consumer behavior and market economics (e.g., online rating/recommendations), as well as political behavior (e.g., 2011-12 uprisings in the Middle East).
Being able to preserve, model, and predict information cascades over online social networks will have many theoretical and practical implications, e.g., for marketing, recommendation filtering, and studying of societal behavior. Massive empirical data sets on users' online social activities are being collected, but they are often too big to analyze. Our grand vision is to build a time capsule that can condense pertinent information about online social activities (derived from massive data from multiple sources) as user interactions evolve through time. Such digests should be compact enough to store and process, yet contain rich enough information to support a wide range of queries or hypothesis testings in future. Towards this end, we propose to analyze online social activities by studying the associated user activity graphs (UAGs), in which a node represents a user, and an edge represents a specific action. Our goal is to design graph generative models and summarization techniques that can preserve pertinent information about these UAGs, including macro-level dynamic growth/evolution of the graphs, as well as micro-level user influence in the recruitment process. In addition, the project will design graph summarization techniques that preserve pertinent information to answer a common set of queries, such as identifying the most "collectively influential" users.
We will build on some of the results and insights gained in our earlier OSN project on measuring and characterizing large-scale online social networks and applications.
- Chen-Nee Chuah, UC Davis - ECE (PI)
- Raissa D'Souza, UC Davis - CS & MAE (Co-PI)
- Jun (Jim) Xu, Georgia Institute of Technology (external Co-PI)
- Stratis Ioannidis, Technicolor Palo Alto Lab
- Balachander Krishnamurthy, AT&T Labs-Research
- Jinyoung Han
- Mohammad Rezaur Rahman, CS (PhD)
- (Sally) Yiwei Sun, ECE (MS)
- Zejun Huang, ECE (MS)
- Han Liu, ECE (PhD, 2014)
M. R. Rahman, C-N. Chuah, "Can Sampling Preserve Application Adoption Process of OSN Graphs?" Abstract & Poster, NetSci, June 2-6 2014. [pdf]
M. R. Rahman, P-A. Noel, C-N. Chuah, B. Krishnamurthy, and R. M. D'Souza, "Peeking into Invitation-based Adoption Process of OSN-based Applications," ACM Computer Communication Review (CCR), vol. 44, no. 1, pp. 21-27, January 2014. (Best of CCR, to be presented at ACM SIGCOMM 2014) [pdf]
H. Liu, A. Nazir, J. Joung, and C-N. Chuah, "Modeling/ Predicting the Evolution of User Activity Graphs on OSN-based Applications," WWW, May 2013. [pdf]
H. Liu, Measuring and Modeling the Cascades/Diffusions through Online Social Networks, PhD dissertation, Electrical & Computer Engineering, UC Davis, 2014.
See more publications from our previous OSN project here.
This project is supported by National Science Foundation CNS-1302691 grant (2013-2016).