SSIE 641: Advanced Topics in Network Science

Course Materials

Copyright 2018 by Hiroki Sayama


Course overview

Network science, expanding rapidly within the last few decades, has been causing a major paradigm shift in the way science is being done in the 21st century. It provides the concepts and tools to help us understand various complex systems in nature, society and technology from a viewpoint of connection and interaction. Topics range from the way cells function and create living things, to the ways neurons connect and configure the way brains and minds work, to the ways societies and economies grow and behave. It has become an essential body of knowledge for scientific research in a wide variety of fields, including engineering, physics, computer science, biology, medicine, and social/organizational sciences.

This course provides students with concepts and mathematical/computational tools developed in network science, for modeling, analyzing and simulating the structures and dynamics of various complex networks. Specific topics to be discussed will include: Complex network topologies, methods for network analysis, visualization and simulation, models of dynamical/adaptive networks, techniques for mathematical analysis, network stability and robustness, and applications to social, biological and engineering systems. Python and NetworkX will be used for modeling and analysis of complex networks, in addition to other computational tools. Students should have a reasonable amount of experience in Python programming.

Schedule, slides & reading materials

Other recommended readings

Link to Spring 2017 version

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