Course description and objectives
The course is an interdisciplinary introduction to the emerging science of complex networks and their applications. Topics to be covered include the mathematics of networks (graph theory), data analysis, and applications to biology, sociology, technology, and other fields. Students will learn about the ongoing research in the field, and ultimately apply their knowledge to conduct their own analysis of a real network data set of their choosing as part of the final project.
Lectures: Lectures will be given by Prof. Barabási, by Drs. Towlson and Cornelius
Homework: There will be three (3) homework assignments representing a mix of mathematical work and computational data analysis. Students are expected to turn in their source code for the computational exercises.
Examinations: The final exam will be a complete study of a real network, from data collection to network analysis. In place of a midterm exam, there will be an intermediate presentation to check your progress on this project and provide feedback.
We are surrounded by systems that are hopelessly complex, from the society, a collection of seven billion individuals, to communications systems, integrating billions of devices, from computers to cell phones. Our very existence is rooted in the ability of thousands of genes to work together in a seamless fashion; our thoughts, reasoning, and our ability to comprehend the world surrounding us is hidden in the coherent activity of billions of neurons in our brain.View slidesHandoutHandoutHomework 2
Networks and graphs: In its simplest form, a network is a set of nodes connected by links. A graph, however, is a set of vertices connected by edges? Do you sense the difference? Well, regarding the common usage of these terms, there is none: graph and networks these days are used interchangeably, and so do terms of nodes and vertices and links and edges.View slidesHandoutHandoutHomework 2