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.
These webpages are being prepared for the fall 2018 edition of the course.
Lectures: Fridays from 11:00 to 12:45 (Sep. 7 - Dec. 7)
Snellius room TBA
Lab sessions (not every week): Fridays from 9:00 to 10:45 in room 302/304
Lecturer: dr. Frank Takes - firstname.lastname@example.org, room 157b
Course assistant: Anna Latour MSc - email@example.com, room TBD
Teaching assistants: Antonio Barata MSc (firstname.lastname@example.org), room 150 and [TBD]
Prerequisites: a CS bachelor with 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 with 1458 nodes and 1948 edges.
|Date||First slot (9:00-10:45)||Second slot (11:00-12:45)|
|1.||Sep 7, 2018||No activities||Lecture 0: Course organization
Lecture 1: Introduction and small world phenomenon
|Oct 2, 2018||Deadline for Assignment 1|
|Oct 30, 2018||Deadline for Assignment 2|
|Nov 16, 2018||Deadline for the first half of the course project paper (for the peer review session)|
|Nov 23, 2018||Deadline (optional) for preliminary version of course project paper|
|Nov 30, 2018||Deadline for a substantial amount of code / experimental pipeline of the course project (for the code review session)|
|Dec 12, 2018||Deadline for the final version of the course project paper|
|Dec. 21, 2018||Course end. Grades will be submitted to the student administration|
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).