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This page is part of the course information of a discontinued course. It is kept for historical reasons only.

TSKS11 Networks: Models, Algorithms and Applications

Erik G. Larsson
Erik G. Larsson

Course Director

TSKS11 Networks: models, algorithms and applications is an inter-disciplinary course on network science. Network science has applications in the analysis of, for example, social networks, information networks, computer/communication networks and biological networks.

Course themes:

  • Models and representations of networks, adjacency matrix, degree distribution, modularity, Laplacian
  • Weighted and signed networks, structural balance ("friends and foes"), homophily, betweenness
  • Centrality metrics: "who is most influential" (Google PageRank, Katz, hub/authority, closeness)
  • Algorithms for network partitioning and detection of communities
  • Network models, random (Poisson) networks, scale-free networks, degree correlations
  • Power laws, growth models, information cascades and viral phenomena
  • Network formation and growth, preferential attachment, percolation
  • World-is-small phenomena, searchability and reachability ("six-degrees-of-separation")
  • Diffusion, random walks and cascades on networks
  • Collaborative filtering on bipartite graphs (recommendation system)

The course consists of a series of lectures, tutorials, and a number of hands-on computer sessions. In these sessions we will:

  1. Analyze and visualize large networks using software tools, especially Gephi and SNAP. We will be using synthetic data and real data from social networks, among others SNAP datasets, and the DBLP database. The focus is on the interpretation and understanding of the results.
  2. Implement and experiment with some algorithms for network analysis, specifically centrality metric (incl. Google PageRank) computation, and network partitioning. The purpose is to obtain a deeper understanding for how these algorithms work.
  3. Implement a recommendation system that uses collaborative filtering to suggest movies to viewers based on grades given by other viewers (similar to what is being used by services such as NetFlix). We will train and evaluate the performance of this system using real data from the "Movielens" project, consisting of millions of grades set by real (but anonymized) users on real movies. The focus is on the ability to implement a basic algorithm, and propose and evaluate improved algorithms.


Documents and files:


  • Follow this link to import the course schedule into your calendar.
  • Use this link to see then the computer labs are unreserved and can be used for independent work.

Main course textbook (compulsory):

  • A. Barabasi, Network Science, Cambridge University Press, 2016.
    Available online and in print from various bookshops, for example here

We will also be using some material from the following texts:

  1. D. Easly and J. Kleinberg: Networks, Crowds, and Markets: Reasoning About a Highly Connected World, Cambridge University Press, 2010.
    Available online at the authors' website.
  2. M. Chiang, Networked Life, Cambridge University Press, 2012.
    Available as e-book via this link (LiU-logon required).

Here are some other books that also are relevant for the course:

  1. M. Newman, Networks: An Introduction, Oxford University Press, 2010.
  2. E. Estrada and P. A. Knight, A First Course in Network Theory, Oxford University Press, 2015.
  3. V. Latora, V. Nicosia, G. Russo, Complex networks, Cambridge University Press, 2017.
  4. E. Estrada, The Structure of Complex Networks: Theory and Applications, Oxford University Press, 2011.

Relevant links:

Page responsible: Erik G. Larsson
Last updated: 2020 02 11   11:46