The curriculum provides students with a strong foundation in network science via four core courses, along with substantive expertise via at least three courses in a concentration, and research experience via two research rotations with network science faculty.
Complex Networks and Applications
This course provides an overview of theories and analytical approaches in Network Science.The course is an interdisciplinary course, focused on the emerging science of complex networks and their applications. The material includes the mathematics of networks, their applications to biology, sociology, technology and other fields, and their use in the research of real complex systems in nature and in man-made systems. Students will learn about ongoing research in the field, and apply their knowledge in the analysis of network models.
Network Science Data
This course provides an overview of data mining and analysis and other techniques in Network Science. The course introduces students to network data analysis, including algorithms for the characterization and measurement of networks (centrality, decomposition, community analysis etc.); issues in sampling and statistical biases; visualization algorithms; and software tools. Students will learn about working with real-world network datasets.
Dynamical Processes on Complex Networks
This course focuses on the modeling of dynamical processes (contagion, diffusion, routing, consensus formation etc.) in complex networks. The course partly consists of guest lectures from local and national experts working in process modeling on networks. Dynamical processes in complex networks provide a rationale for understanding the emerging tipping points and nonlinear properties that often underpin the most interesting characteristics of socio-technical systems. The class reviews the recent progress in modeling dynamical processes that integrates the complex features and heterogeneities of real-world systems.
Social Network Analysis*
This course provides the basic methodology, techniques and theory developed in the analysis of social networks. The course offers an overview of the research on networks in the social sciences, focusing on the literatures covering social influence, diffusion, and persuasion; social capital, and collective action, drawing from sociology, political science, and economics. Students will learn the tools of analysis — in R, Gephi, and Python — as well as the skills necessary for a social sciences approach to networks, such as causal inference and measurement.
Network Data Mining*
This course provides students with knowledge of specific data mining techniques of large scale information networks and large scale network datasets. The class focuses on network representations, different types of networks (document networks and the web, social networks, microblogging networks), community detection, search and topical locality in information networks, intelligent walks on a graph (smart web crawlers), similarity and link prediction, missing link discovery, node and edge classification, node attribute inference, ranking (HITS, pagerank), diffusion (viral prediction), privacy and re-identification.
* Students will select one of these two core courses, although the other may be taken as part of a concentration.
Courses within concentrations
The following are some of the courses that may be applied towards the various concentrations. These are subject to change, and additional courses will be added, or may be proposed to be added by students.
PHYS 7305 Statistical Physics
PHYS 5318 Principles of Experimental Physics
PHYS 7321 Computational Physics
PHYS 7731 Biological Physics
NRSG 5121 Epidemiology and Population Health
PHTH 5202 Epidemiology
PHTH 5224 Social Epidemiology
POLS 7200 Perspectives on Social Science Inquiry
POLS 7201 Methods of Analysis
POLS 7202 Quantitative Techniques
CS5800 Algorithms (Master level) OR CS7800 Advanced Algorithm (PhD level)
CS5200 Introduction to database systems
CS6240 Parallel Processing/Map Reducing
CS6220 Data Mining Techniques (Prereq: CS5800 or CS7800)
CS6140 Machine Learning (Prereq: CS5800 or CS7800)