Complex networks are large real-world graphs or networks with complicated structural 
properties. The study of complex networks cuts across computer science, physics, 
mathematics, life sciences, statistics, social sciences, and other areas. 
Typically, a complex network is characterized by a number of properties, such as 
degree sequence, node centrality, clustering coefficient, and community structure. 
These properties raise issues in how to generate random networks with specific properties, 
which in turn brings in the mathematical theory of random graphs. There are also 
algorithmic questions surrounding how to measure these properties computationally. 
Algorithms must be efficient, given the great size of complex networks, so approximations 
are sometimes allowed and distributed algorithms are common. Another research area is the 
dynamic spread of information or contagion over a complex network. 

Given the instructor's interest in computational epidemiology, topics from that area may 
be considered. In this course, we will study the above through targeted lectures, 
readings from the selected textbook and the primary literature, and one or more computational 
or mathematical projects. The selection of papers will reflect the interests of the instructor 
and the students. Students can expect to lead the discussion of papers, perhaps in teams. 

The instructor will select the primary textbook from one of the potential textbooks listed below. 
Inquiries to heath@vt.edu.  

Potential Textbooks: 

• Complex Networks: Principles, Methods and Applications 

• Networks, Second Edition, Mark Newman 

• Random Graphs and Complex Networks, Volume 1