Скачать или смотреть Modeling Complex Social Networks: Challenges & Opportunities for Statistical Learning & Inference

Center for Science of Information presents
as part of our Weekly Seminar series:

Assistant Professor Jennifer Neville
Purdue University, Depts. of Computer Science & Statistics

"Modeling Complex Social Networks: Challenges & Opportunities for Statistical Learning & Inference"

Recorded Wednesday, April 27, 2011

Abstract: Recently there has been a surge of interest in methods for analyzing complex social networks: from communication networks, to friendship networks, to professional and organizational networks. The dependencies among linked entities in the networks present an opportunity to improve predictions about the properties of individuals, as birds of a feather do indeed flock together. For example, when deciding how to market a product to people in MySpace or Facebook, it may be helpful to consider whether a person's friends are likely to purchase the product.

This talk will give an overview of the area, presenting a number of characteristics of social network data that differentiate it from traditional inference and learning settings, and outline the resulting opportunities for significantly improved inference and learning. We will discuss techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues, statistical challenges, and potential modeling pathologies that are unique to network data.