LE/EECS 4414 (3.00)

Information Networks

Description

Information networks are effective representations of pairwise relationships between objects. Examples include technological networks (e.g., World Wide Web), online social networks (e.g., Facebook), and biological networks (e.g., Protein-to-Protein interactions). The study of information networks is an emerging discipline of immense importance that combines graph theory, probability and statistics, data mining and analysis, and computational social science. This course provides students with both theoretical knowledge and practical experience of the field by covering models and algorithms of information networks and their basic properties. In addition, analysis of information networks provides the means to explore large, complex data coming from vastly diverse sources and to inform computational problems and better decisions. Topics include: basic graph theory, network measurements, network models, community detection, graph partitioning, link analysis, link prediction, information cascades & epidemics, influence maximization, network ties, recommendation systems, mining graphs, and connections to problems in the social sciences and economics. Prerequisites: cumulative GPA of 4.50 or better over all major EECS courses (without second digit "5"), LE/EECS 3421 3.00, LE/EECS 3101 3.00, SC/MATH 2030 3.00 or SC/MATH 2930 3.00. Date of submission: 2017-03-07

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