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Course overview

Course description

Network science is a unifying framework to study complex systems, such as living organisms, societies, and many techno-social systems. Therefore, understanding networks and network (graph) data is fundamental to numerous domains. This graduate-level course introduces the fundamental concepts as well as key applications of network science for a broad range of students. Topics include statistical properties and models of real-world networks, network data & algorithms, how information and diseases spread in our society, and machine learning with networks — e.g., community detection (clustering) and graph embedding.

Because your learning should be the primary focus, please engage actively by voicing your confusions, challenges, and intriguing digressions! Rather than merely watching lectures, think critically, engage in debates, and immerse yourself.

Course objectives

By the end of the course, students are expected to be able to identify, construct, model, and analyze networks by choosing and applying appropriate methods and algorithms, as well as understanding ethical issues surrounding network data. Students are also expected to be able to explain, both mathematically and conceptually, the key network concepts, algorithms, models, and statistical properties, as well as their implications.

Basic Information

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