Tanya Berger-Wolf

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  Computational Analysis of Dynamic Social Networks: from Inference to Insight
  Tanya Berger-Wolf
  From gene interactions and brain activity to cellphone calls and zebras grazing together, large, noisy, and highly dynamic networks of interactions are everywhere. Unfortunately, in this domain, our ability to analyze data lags substantially behind our ability to collect it. Moreover, we may be collecting the wrong data for the questions we want to answer in the first place. From collecting the data and inferring the networks to producing meaningful insight at scale, challenges are there every step of the way and computational approaches have been developed to meet those challenges.
  In this talk I will show computational approaches that address some of the questions about dynamic interaction networks: whom should we sample? how often? what is the "right" implicit network? what are the meaningful patterns and trends? and how can we use the network to gain insight into other aspects of the node behavior? The methods leverage the topological graph structure of the networks and the size of the available data to, somewhat counter-intuitively, to produce more accurate results faster. We will demonstrate the scientific implications of the computational analysis on networks of zebras, baboons, and interacting brains cells.