• 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.
      SYLLABUS/CONTENT

      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.

      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
      GRAPH ANALYTICS
      https://dibris.aulaweb.unige.it/
  • Who
  • How
  • Where and when
    • URL Aula web
      GRAPH ANALYTICS
      https://dibris.aulaweb.unige.it/
      OFFICE HOURS FOR STUDENTS
      Marina Ribaudo

      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

  • Contacts