Location: EU Supported » PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning » 26th International Conference on Machine Learning (ICML), Montreal 2009 » Tutorials

Modeling Social and Information Networks: Opportunities for Machine Learning

author: Jure Leskovec, Computer Science Department, Stanford University
published: Aug. 26, 2009,   recorded: June 2009,   views: 25670
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Description

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.

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