This course deals with computer science (CS) aspects of social network analysis (SNA), and is open to all students in the master computer science programme at Leiden University.


News
- The first lecture is on Friday September 8 at 11.00 in Snellius room B02.
- Interested students not from the Leiden CS programme should contact the lecturer before the start of the course.
- These pages are currently being prepared for the 2017 fall semester.

Course information

Lectures: Fridays from 11:00 to 12:45 (Sep. 8 - Dec. 1) in Snellius room B02
Lab sessions (not every week): Fridays from 9:00 to 10:45 in room 302/304
Lecturer: dr. Frank Takes - f.w.takes@liacs.leidenuniv.nl, room 157b
Teaching assistants: Hanjo Boekhout BSc (h.d.boekhout@umail.leidenuniv.nl) and
Jesper van Engelen BSc (j.e.van.engelen@umail.leidenuniv.nl)
Prerequisites: a CS(-related) bachelor; mainly courses on Algorithms, Data Structures and Data Mining
Literature: provided papers and book chapters (free and digitally available)
Examination: based on presentation, paper, programming, peer review and participation (no exam)
Study points: 6 ECTS

[Network visualization image]

Network with 1458 nodes and 1948 edges.


Course schedule

  Date First slot (9:00-10:45) Second slot (11:00-12:45)
1. Sep 8, 2017 Lecture 1: Introduction and small world phenomenon

Course description

See the e-Studyguide for a more general description.

Topics include: SNA from a CS perspective (graph representation, complexity issues, examples), Graph Structure (power law, small world phenomenon, clustering coefficient, hierarchies), Paths and Distances (neighborhoods, radius, diameter), Spidering and Sampling (BFS, forest fire, random walks), Graph Compression (graph grammars, bitwise tricks, encryption, hashing), Centrality (degree centrality, closeness centrality, betweenness centrality, rating and ranking), Centrality and Webgraphs (HITS, PageRank, structure of the web), Community Detection (spectral clustering, modularity), Visualization (force-based algorithms, Gephi, NodeXL), Graph Models (random graphs, preferential attachment), Link Prediction (structure, semantics, prediction algorithms, graph mining), Contagion (diffusion of information, spreading activation, gossipping) and Privacy and Anonymity ((de-)anonymizing graphs, ethical aspects, privacy issues).

The course was also given in 2014, 2015 and 2016.