Aller au contenu  Aller au menu Aller à la recherche

Navigation principale

accès rapides, services personnalisés

Rechercher

Recherche détaillée

Contact

Prométhée Spathis
Head of the CNI program at SU

courriel : master.info.digit-cni@upmc.fr

 

 

Network Analysis and Mining

 

Person Responsible for Module (Name, Mail address):

Lionel Tabourier

lionel.tabourier@lip6.fr

Credit Points (ECTS): 6 Module-ID: MU5IN062
University: Sorbonne Université Department: Master Informatique

Prerequisites for Participation

  • Knowledge of at least one programming language.
  • Basis of graph algorithmics is recommended but not necessary.

Intended Learning Outcomes

  • Students who have successfully finished this module will be able to:
  • Describe real world interaction and relation datasets in terms of the fundamental notions of the complex networks field.
  • Code efficiently and run standard network measurements on real world interaction and relation datasets.
  • Provide a critical analysis of research papers on the topic.

Content

The course is about the information that can be drawn from graph models of various real world networks. The networks under consideration may concern very  different fields : computer science (Internet, www, P2P networks), sociology (social networks, commercial networks), biology (protein/DNA interaction networks) etc. They share the property of being self-organized, in the sense that there is no plan of the global structure. Consequently, they exhibit common structural and dynamic properties that we measure and describe. We also try to understand how these properties impact the functioning of the network.

The course deals mostly with two types of practical examples: social networks and the Internet network at the IP level. Each course is either a presentation of transversal notions or a discussion on a specific research issue. Among other topics, we will discuss small-world effect in social networks, biases in the measurements of the Internet topology, detection of communities in networks.

The topic being very interdisciplinary, one of the most important skill required is having scientific curiosity for questions out of computer science.

Teaching and Learning Methods

  • 2h weekly hours lecture
  • 2h weekly hours integrated interactive tutorials (problem solving, assignments discussion, lab sessions)

Assessment and Grading Procedures

2 home assignments (40%), and final written exam (60%)

Workload calculation (contact hours, homework, exam preparation,..)

  • 4h weekly contact hours x 14 weeks = 56 h
  • 5h weekly hours preparation and afterwork x 14 weeks = 70 h
  • Exams preparation: 24h
  • Total: 150 h

Frequency and dates

Offered every Fall semester:

  • Classes start mid-September and end end-January;
  • Final exam at the beginning of February;
  • Makeup exam for those who failed the first session in next September.

Max. Number of Participants

30

Enrolment Procedures

Request to the head of CNI program

Recommended Reading, Course Material

  • M.E.J. Newman: The structure and function of complex networks. SIAM review, 45(2), 2003.
  • P. Holme and J. Saramäki: Temporal Networks. Springer, 2013.