08-801 Dynamic Network Analysis
12 units, Carnegie Mellon University
Who knows who Who knows what Who communicates with whom Who is influential How do ideas, diseases, and technologies propagate through groups How do social media, social, knowledge, and technology networks differ How do networks evolve How do networks constrain and enable behavior How can a network be destroyed or made resilient Such questions can be addressed using network science. Network Science, also referred to as social network analysis and link analysis, is an interdisciplinary field aimed at understanding complex behavior using networks. Graph theoretic, statistical and simulation techniques are used. Applications areas are limited only be your imagination. This course covers the basics of network science from an interdisciplinary perspective. Key methods, theories, constraints on data collection, research findings and challenges are examined. Application areas discussed include social media e.g. twitter, disaster response, organizational networks, citation networks, semantic networks, political elite networks and crises, terror networks, disease and health networks, nationstate networks, and cyber networks. Methods covered include basic centralities, group and community detection, link inference, network change detection, comparative analytics and big data techniques. Questions addressed includeHow do we conceptualize, measure, compare and evaluate various types of networks How do we evaluate the impact of policies and technology on networks What nodes, relations, groups, or motifs stand out or are influential in a network How do networks emerge, evolve, and change Prerequisite Undergraduatelevel statistics course or permission of instructor. Linear algebra is recommended but not required. Students are encouraged to bring and use their own data, or to use publicly available network dataset for assignments.
Prerequisites: none listed
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