Learning algorithms and techniques for large scale graph analytics, including centrality measures, connected components, graph clustering, graph properties for random, small-world, and scale free graphs, graph metrics for robustness and resiliency, and graph algorithms for reference problems.
Lectures, practicals, and individual study.
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.
Epidemic models.
Case study: web, social media, epidemic models.
Complex data visualization using open source software tools.
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.
Ricevimento: By appointement at the DIBRIS Department, room 231, 2nd floor, Valle Puggia,Via Dodecaneso 25, Genova. E-mail: marina.ribaudo@unige.it Phone: 010 353 6631
MARINA RIBAUDO (President)
GIORGIO DELZANNO
GIOVANNA GUERRINI
LORENZO ROSASCO
Oral examination with discussion of the practicals assigned during the course.
Individual interview.
