MIS615

041138

This course will introduce doctoral students to quantitative methods in network science used to model, analyze, and understand various complex systems and the unique interactions among their components. Topics to be covered include the mathematics of networks (graph theory), data analysis, and applications to technology, business, biology, medicine/healthcare, and other relevant fields. Students will learn about ongoing research in the field, and ultimately apply their knowledge to conduct their own analysis of a large real world complex system and corresponding dataset(s) of their choosing as part of the final research paper. You will learn fundamental network theory including, properties of networks, measures and metrics such as centrality, transitivity, reciprocity, homophily, and others. You will cover concepts such as small-world effects, modularity, degree distributions, and assortative mixing. Algorithms on graphs including traversals, and graph partitioning will be explained. You will study random graphs models together with graph clustering methods, small and giant components, power-law distribution, and others. You will also study the concepts of percolation and network reliance, epidemics, and dynamic systems as well as signed networks. Students at the end of the system will have gained knowledge to analyze complex systems by modeling interactions among the components as a network. Students will also learn to analyze these networks by implementing and using various algorithms and visualizing the results of the algorithms and ultimately integrating networks with prediction models. The course is expected to use Python, GEPHI, and SNAP to construct and analyze large datasets using network science.

GRD - Regular Grades A, B, C, D, E

Graduate