Jennifer Neville

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  Statistical methods for modeling network distributions
  Jennifer Neville
  There has been a growing interest in analyzing the network structure of complex systems to understand key patterns/dependencies in the underlying system. This has fueled a large body of research on both models of network structure and algorithms to automatically discover patterns for use in predictive models. However, robust statistical models, which can accurately represent distributions over graph populations, are critical to assess the significance of discovered patterns or to distinguish between alternative models. Moreover, efficient sampling and inference algorithms are crucial for tractable analysis in large-scale domains evolving over time. However, unlike metric spaces, the space of graphs exhibits a combinatorial structure that poses significant theoretical and practical challenges to accurate estimation and efficient sampling/inference. In this talk, I will discuss our recent work on modeling distributions of networks and outline how the methods can be used for hypothesis testing, anomaly detection, and anonymization.