Speaker: Mark Newman, University of Michigan, USA

Title: Patterns and surprises in rich but noisy network data

Abstract: There has in recent years been considerable interest in data that take the form of networks, sets of objects or people and the patterns of links or interactions between them.  Widely studied examples include the World Wide Web, citation networks, social networks, and biological networks such as metabolic networks and food webs.  Empirical observations of networks like these are often noisy, containing measurement error, contradictory observations or missing data, but they can also be richly structured, with measurements of different types, repeated observations, annotations or metadata.  In this talk I will address the problem of making best estimates of network structure from such rich but noisy data, particularly focusing on social and biological examples.  In the process, we will see that the pattern of errors in network data is far from random and can teach us some intriguing lessons not only about the data but also about the underlying systems they describe.

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