Social Networks

Online social networks have revolutionized the way we interact and share information over the Internet; social networking applications such as YouTube, Twitter, Facebook, Snapchat etc., have millions of active users. Multiple terabytes of information is generated daily as a result of user interactions in such networks. The ability to collect and analyze such data provides unique opportunities to understand the underlying principles of social networks, their formation, evolution and characteristics.

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The focus of our research is to study how information is being generated and shared in social networks, as well as design novel social networking applications and tools to study and understand social networks. In particular, we are interested in

Algorithms: Design of novel algorithms, algorithms for analyzing social networks, as well to improve the performance of information sharing in social networks.

Systems: Development of new systems to harvest, collect and analyze data from online social networks, as well building novel social networking applications.

User Behavior: Understanding the user behavior in social networks, in particular understanding incentives for users to form and participate in social networks, as well as understand the importance of communities, influence and reputation in social networks.

Our research combines mathematical models and analysis with data analytics and processing of large sets of data obtained from online social networking applications.  The research on mathematical models and analysis includes studying  how online social networks are formed and evolve over time, the graph structure of social networks, how information is being created and distributed, as well as using these models to design algorithms to make the sharing of information is social networks more efficient. For this we combine techniques and methods from probability, optimization, graph, and game theory. The research on data analytics includes building the technology to harvest and collect large amounts of data from online social networks, researching efficient ways to store, process, analyze and support interactive queries on large collections of dynamically evolving data sets and designing suitable algorithms to process massive data sets in near real time. Building systems that showcase our techniques and ideas is at the centre of our work.

Faculty Members:

Ashton Anderson
Nick Koudas
Peter Marbach