Modeling Social and Information Networks: Opportunities for Machine Learning
published: Aug. 26, 2009, recorded: June 2009, views: 3234
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Emergence of the web, social media and online social networking websites gave rise to detailed traces of human social activity. This offers many opportunities to analyze and model behaviors of millions of people. For example, we can now study ''planetary scale'' dynamics of a full Microsoft Instant Messenger network of 240 million people, with more than 255 billion exchanged messages per month. Many types of data, especially web and "social" data, come in a form of a network or a graph. This tutorial will cover several aspects of such network data: macroscopic properties of network data sets; statistical models for modeling large scale network structure of static and dynamic networks; properties and models of network structure and evolution at the level of groups of nodes and algorithms for extracting such structures. I will also present several applications and case studies of blogs, instant messaging, Wikipedia and web search. Machine learning as a topic will be present throughout the tutorial. The idea of the tutorial is to introduce the machine learning community to recent developments in the area of social and information networks that underpin the Web and other on-line media.
Download slides: icml09_leskovec_msain_01.pdf (17.7 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !