"Prediction in Complex Networks" (Next Week at the Statistics Seminar)

All of the statistics department's seminars are, of course, fascinating presentations of important work, but next week's could hardly be more relevant to my interests if I had arranged it myself.

Jennifer Neville, "Prediction in complex networks: The impact of structure on learning and prediction"

Abstract: The recent popularity of online social networks and social media has increased the amount of information available about users' behavior — including current activities and interactions among friends and family. This rich relational information can be exploited to predict user interests and preferences even when individual data is sparse, as the relationships are a critical source of information that identify potential statistical dependencies among people. Although network data offer several opportunities to improve prediction, the characteristics of real world datasets present a number of challenges to accurately incorporate relational information into machine learning algorithms. In this talk, I will discuss the effects of sampling, parameter tying, and model roll-out on the properties of the resulting statistical models — which occurs through a complex interaction between local model properties, global network structure, and the availability of observed attributes. By understanding the impact of these interactions on algorithm performance (e.g., learning, inference, and evaluation), we can develop more accurate and efficient analysis methods for large, partially-observable social network and social media datasets.

Place and time: Scaife Hall 125, 4--5 pm on Monday, 8 April 2013

Enigmas of Chance; Networks