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Learning from common connections in social networks

Interactions through social networks have been in constant rise for the past decades especially through Facebook, LinkedIn, Snapchat, Twitter and many such network portals. One wonders how to optimally disperse information in such networks. Understanding the structure of the network is critical to achieving this goal. The popular social networks are usually huge (in the order of a few millions, if not billions of participants) and hence there is a need to identify network or graph structures in these complex networks quickly and possibly via a reasonably small sample. The aim of the project is two-fold: first, we seek to understand the growth in networks for various rates of growth of the edges and other network characteristics; and secondly, we want to propose inferential techniques for retrieving key information about these networks under the purview of such models.

Postdoctoral position

Postdoctoral research position(s) are available under this project. Potential candidates are expected to have knowledge of statistical analysis and/or probability theory with interest in random graphs and/or heavy-tails and extremes.

Research assistantship and PhD positions are also available under the same project.
Apply directly to the ESD PhD program if you are interested in pursuing a PhD with interest in applied probability, data science and related fields. ESD PhD

For further information, send me an email.