TSKS33 Complex networks and big data
TSKS33 Complex networks and big data deals with the way entities are connected. These entities may be computers or routers (in the Internet), webpages (in the World Wide Web), people (in social networks), or cities (in a transportation network), for example.
- Models and representations of networks, adjacency matrix, degree distribution
- Network motifs
- Laplacian operator
- Bipartite and tripartite networks, weighted and signed networks, structural balance, similarity measures
- Centrality metrics: (Google PageRank, Katz, hub/authority, closeness)
- Sampling on networks, random walks and friendship paradoxes
- Assortative mixing metrics, degree correlations and modularity
- Algorithms for network partitioning and community detection
- Network models, random (Poisson) networks, configuration model
- Power laws and scale-free networks, preferential attachment and other growth models
- World-is-small phenomena ("six-degrees-of-separation"), searchability and reachability
- Dynamics on networks, diffusion and cascades
- Introduction to graph learning
The course consists of a series of lectures, tutorials, and a number of hands-on computer sessions.
- Course director and lecturer: Erik G. Larsson
- Tutorial and lab assistant: TBD
Course textbooks (compulsory):
- V. Latora, V. Nicosia, G. Russo, Complex networks: Principles, methods and applications, Cambridge University Press, 2017.
- TSKS33 course notes by E. G. Larsson, available online
Here are some other books that also are relevant for the course:
- F. Menczer, S. Fortunato and C. A. Davis, A First Course in Network Science, Cambridge University Press, 2020.
- A. Barabasi, Network Science, Cambridge University Press, 2016.
- M. Newman, Networks: An Introduction, Oxford University Press, 2010.
- E. Estrada and P. A. Knight, A First Course in Network Theory, Oxford University Press, 2015.
- E. Estrada, The Structure of Complex Networks: Theory and Applications, Oxford University Press, 2011.
- 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.
- Gephi, software for the analysis of large networks
- Stanford Network Analysis Project (Python/C++ library for analysis of large networks, and data sets)
- Course description in the LiTH study guide
Erik G. Larsson
Last updated: 2020 06 17 10:28