Jennifer Neville, Purdue University
Foster Provost, New York University
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 inference about 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 tutorial will explore the unique opportunities and challenges for modeling social network data. We will begin with a description of the problem setting, including examples of various applications of social network mining (e.g., marketing, fraud detection). We will then present 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 specific techniques for capitalizing on each of the opportunities in statistical models, and outline both methodological issues and potential modeling pathologies that are unique to network data. We will give links to the recent literature to guide study, and present results demonstrating the effectiveness of the techniques.
Prerequisites: The tutorial assumes a basic knowledge of AI-style inference and machine learning, equivalent to an introductory graduate or advanced undergraduate class.