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 datasets [2,8,29,31]; statistical models for modeling large scale network structure of static [1,4,15,18,28] and dynamic [14,22,25] networks; properties and models of network structure and evolution at the level of groups of nodes [3,26] and algorithms for extracting such structures [6,9]. I will also present several applications and case studies of blogs, instant messaging, Wikipedia and web search [17,19,21,30,32].
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.
Video of the tutorial at ICML 2009.
Slides for the tutorial. (PDF, 17mb)
- Introduction [15 min]
- Examples of networks and motivating applications
- Why should machine learning community care about networks?
- Network structure and models [45 min]
- Characteristics of large networks, differences and similarities between different types of networks [8, 27, 29, 31]
- Models of macroscopic network structure [1, 2, 4, 15, 28, 32]
- Estimating models from network data [2, 16, 33]
- Navigation and search in networks [16, 32]
- Microscopic and macroscopic properties of evolving networks [13, 14, 22, 25]
- Models of network evolution: [10, 12, 22, 25]
- Link prediction in networks [6, 19]
- Evolution of groups of nodes in networks: formation, evolution and group membership in networks [3, 34]
- Algorithms and models for community structure in networks [6, 9, 26, 30]
- Properties of community structure in large networks and implications for machine learning in graphs 
- Current research directions
- Open problems and research opportunities
- Connections to economics, social and cognitive sciences, and game theory.
Presentation slides will be posted prior the tutorial.
Who should attend:
Since network data arises in so many different areas of data mining and machine learning, this tutorial should be of theoretical and practical interest to a large part of the machine learning community. The tutorial will not require prior knowledge beyond the basic concepts covered in introductory machine learning and algorithms classes.
Jure Leskovec is an assistant professor in Computer Science at Stanford University currently on leave at Cornell University. Jure received his PhD in Machine Learning from Carnegie Mellon University in 2008. He received the ACM KDD 2005 and ACM KDD 2007 best paper awards, won the ACM KDD cup in 2003 and topped the Battle of the Sensor Networks 2007 competition. Jure holds three patents. His research focuses on the analysis and modeling of large real-world social and information networks as the study of phenomena across the social, technological, and natural worlds.
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