Computing That Serves

Mining Information Networks the Meerkat Way


Thursday, November 18, 2010 - 11:00am


Osmar Zaïane
McCalla and Killam Professor
Scientific Director of the Alberta Ingenuity Center for Machine Learning
Department of Computing Science
University of Alberta


Christophe Giraud-Carrier

Conventional data is typically considered as a collection of independent observations identically distributed in the space of possible attribute values. This is known as IID data, and most data mining and data management techniques are based on these assumptions. In reality data are packed-full with all sorts of relationships themselves maintaining their own attribute values. For instance, in a database of customers, these patrons are considered independent and when clustered, this independence is assumed and only the attribute values are taken into account. Customers, however, can have various relationships such as friendship, kinship, neighbours, colleagues, etc. These relationships have significant influence on their behaviour and thus should be reflected in data analysis. Much data is now in this form of networked entities such as in biology, criminology, sociology, marketing, etc. Studying information networks is also known as social network analysis.  Social network analysis is a field of study attempting to understand and measure relationships between entities in networked information.

We introduce social network analysis and examine some work done in this area, particularly the application of community mining, We also discuss some open problems pertaining to social network analysis. We will illustrate some practical examples drawn from the e-learning domain.


Osmar R. Zaiane is a Professor in Computing Science at the University of Alberta, Canada. He joined the University of Alberta in July of 1999 after obtaining a Master's degree in Electronics at the University of Paris, France, in 1989 and a Master's degree in Computer Science at Laval University, Canada, in 1992 as well as a Ph.D. from Simon Fraser University, Canada, in 1999 under the supervision of Dr. Jiawei Han. His Ph.D. thesis work focused on web mining and multimedia data mining. He has research interests in novel data mining algorithms, web mining, text mining, image mining, social network analysis and information retrieval with application in health informatics and educational data mining. Dr. Zaiane has published more than 100 papers in refereed international conferences and journals and taught on all six continents. He is currently the secretary-treasurer of the ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining) as well as treasurer of the newly formed ACM SIGHIT (Special Interest Group on Health Informatics). He is also serving on the steering committee of the IEEE International Conference on Data Mining for which he was the program chair in 2007 and is general chair for 2011. Osmar Zaiane was the co-chair of the ACM SIGKDD International Workshop on Multimedia Data Mining in 2000, 2001 and 2002 as well as co-Chair of the ACM SIGKDD WebKDD workshop in 2002, 2003 and 2005. He was guest-editor of the special issue on multimedia data mining of the journal of Intelligent Information Systems (Kluwer) and wrote multiple book chapters on multimedia mining and web mining. He served for 5 years as Associate Editor then Editor-in-Chief of the ACM SIGKDD Explorations. He is Associate Editor of the Knowledge and Information Systems, An International Journal, by Springer, and of the International Journal of Internet Technology and Secured Transactions.  Osmar Zaiane received the McCalla and Killam awards in 2009 as well as the IEEE ICDM outstanding service award 2009 and the ACM SIGKDD Service Award 2010.