- Aims and content
- LEARNING OUTCOMES
Students will learn algorithms and techniques to address large scale graph analytics, including: graph analytics theory (centrality measures, connected components, graph clustering); graph properties for random, small-world, and scale free graphs; graph metrics for robustness and resiliency; graph algorithms for reference problems. Students will be involved in project activities.
Background on linear algebra and probability.
Complex networks introduction: examples from biology, sociology, economy, computer science.
Network topology (global and local level): connectivity, clustering, centrality measures, diameter, cliques, communities.
Graph models: random graphs, small-world, scale-free networks.
Graphs robustness and fault tolerance.
Web graph: Markov chains and random walk, ranking, search engines.
Dynamic evolution of graphs.
Case study: web, social media, epidemic models.
Complex data visualization using open source software tools.
- RECOMMENDED READING/BIBLIOGRAPHY
M. E. J. Newman, Networks: An Introduction, Oxford University Press, Oxford (2010)
D. Easley and J. Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World (http://www.cs.cornell.edu/home/kleinber/networks-book/)
A. Barabasi: Network Science (http://barabasilab.neu.edu/networksciencebook/)
A. L. Barabasi, Link. La nuova scienza delle reti, Einaudi 2004 , introductory text (optional)
Scientific papers will be suggested during the course.
- URL Aula web
- Commissione d’esame
90530 - GRAPH ANALYTICS
Marina Ribaudo (President)
- TEACHING METHODS
Lectures, practicals, and individual study.
- EXAM DESCRIPTION
Oral examination with discussion of the practicals assigned during the course.
- ASSESSMENT METHODS
- URL Aula web
- OFFICE HOURS FOR STUDENTS
By appointement at the DIBRIS Department, room 231, 2nd floor, Valle Puggia,Via Dodecaneso 25, Genova.
Phone: 010 353 6631