Modeling and Analysis of Complex Networks

3.0 credits · Graduate

This course provides students with modern network science concepts and mathematical/computational tools for modeling and analysis of various complex networks in both their structures and dynamics. Specific topics to be discussed include; mathematical analysis of network structures and dynamics, statistical properties of complex networks, dynamics *of* networks and dynamics *on* networks, co-evolutionary adaptive networks, epidemic thresholds and phase transitions in networks, temporal networks, multiplex/multilayer networks, higher-order networks, and applications to real-world complex systems problems. Python and NetworkX will be used as the primary tools for network modeling and analysis, in addition to other computational tools introduced and used as needed. Students should have a reasonable amount of experience in Python programming and linear algebra.