C S 575
Introduction to Network Science with Applications
Computer Science
College of Physical and Mathematical Sciences
Course Description
Introduction to current topics in network science including network formation models, information flow over networks, and key network properties.
When Taught
Winter
Grade Rule
Grade Rule 8: A, B, C, D, E, I (Standard grade rule)
Fixed
3
Fixed
3
No Prerequisites
Other Prerequisites
Linear Algebra
Learning Outcome
Students will be able to implement and use Barabasi-Albert, Watts-Strogatz, Erdos-Renyi, and affiliation network-based algorithms.
Learning Outcome
Students will be able to implement and use graph theory concepts applicable to networks: adjacency matrix, adjacency matrix, adjacency lists, graph Laplacian, algebraic connectivity, spectral graph properties.
Learning Outcome
Students will implement and understand how information and disease spread/diffuse over a network: simple contagion and complex contagion/diffusion of innovations.
Learning Outcome
Students will apply graph metrics to understand diffusion dynamics: clustering, density, diameter, algebraic connectivity, degree distribution, distance, modularity, assortativity.
Learning Outcome
Students will be able to implement agent-based models over networks.
Learning Outcome
Students will be able to mitigate/promote network effects by identifying communities, removing edges, adding edges, choosing early adopters.
