This workshop will introduce the theory and practice of basic concepts in network analysis, machine learning, and data mining to make sense of the social and information networks that have been fuelled and rendered accessible by the Internet.
Participants will learn about the structure and evolution of networks, drawing on knowledge from disciplines as diverse as sociology, mathematics, statistics, computer science, economics, and physics.
Interactive demonstrations and hands-on analysis of real-world data sets will focus on a range of tasks: from online network data collection, to identifying important nodes in the network, to detecting communities, to opinion mining and sentiment analysis, to predicting future relationships and social attributes.
Recommended background:
Basic understanding of discrete mathematics and programming languages is recommended but not assumed. There will be a few hands-on tasks using Python, so participants are encouraged to bring their laptops. Interested participants can refer to the free and excellent textbook by Easley and Kleinberg called “Networks, Crowds and Markets”.
About the Speaker:
Yazan Boshmaf is a PhD candidate at the University of British Columbia, Canada. He received his M.Sc. degree in Information Technology from the University of Stuttgart, Germany in 2008. His current research focuses on the security of large social and information networks. Yazan’s research experience spans cross-disciplinary areas such as parallel and distributed systems, machine learning, databases, and ubiquitous computing. He is a holder of various awards and scholarships, including two best paper awards and an institutional doctoral fellowship.