Registration is now available for CS 4379E- Network Science.
CS4379E.251 (CRN: 40148)
Schedule: TR 9:30am-10:50a.m
Instructor: Dr. Chul-Ho Lee
Networks and graphs are everywhere. Examples include social networks, communication networks, transportation networks, and power grids. Their importance is ever growing across diverse applications and disciplines. This course covers fundamental concepts and algorithms in the fields of social network analysis and network science as well as practical aspects of analyzing network-structured data. Topics include graph representations, network visualization, graph algorithms, random graph models, centrality measures, link analysis and ranking algorithms, and community detection and graph partitioning. This course also involves hands-on exercises with C++ and Python programming.
At the end of the course the students should be able to:
- Implement graph representations for networks.
- Implement graph algorithms for network analysis, such as graph traversal, strongly connected components, minimum spanning trees, and shortest paths.
- Employ random graph models to generate networks.
- Identify the most important nodes or edges in a network through centrality measures as well as through ranking algorithms.
- Explain algorithms for community detection and graph partitioning.
- Use network analysis libraries in Python, e.g., NetworkX.
- Manipulate and analyze real-world network-structured datasets.
Deadline: Dec. 17, 2023, midnight