Modeling Complex Social Networks
Modeling Complex Social Networks: Challenges and Opportunities for Statistical Learning and Inference by Jennifer Neville - Machine Learning Summer School at Purdue, 2011.
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.
| Modeling Complex Social Networks |
Machine Learning Summer School at Purdue, 2011