C S 575
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Introduction to Graph Data Science
Computer Science
College of Computational, Mathematical & Physical Sciences
Course Description
Use graph structures to explore labeled data and discover network relationships within the data. Understand network relationships, learn graph models of common network structures, identify network communities and influential members, and work with knowledge graphs.
When Taught
Winter
Fixed
3
Fixed
3
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.
