"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;
Posted at April 04, 2013 13:29 | permanent link