TSKS33 Complex Networks and Big Data
TSKS33 Complex Networks and Big Data deals with the way objects are connected. These objects may be computers or routers (in the Internet); webpages (in the World Wide Web); patents, legal and scientific documents that cite one another; people (in social networks); gas pipes or other infrastructure; cities (in a transportation network); who-eats-whom in an ecosystem; or proteins that interact in biology. The focus is on the mathematics of the networks (graphs) that represent such connected systems, and on algorithms that operate on these networks.
- 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
- Course director and lecturer: Erik G. Larsson
- Assistants: Daniel Perez Herrera, Ahmet Kaplan, Jianan Bai and Sai Thoota
- Required reading
- V. Latora, V. Nicosia and G. Russo, Complex networks: Principles, methods and applications, Cambridge University Press, 2017.
- Lecture notes by E. G. Larsson.
- Other books that are also relevant (but not required) 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.
- M. Chiang: Networked Life, Cambridge University Press.
- Supplementary material: PowerPoint slides, answers to the tutorial problems, and material for the labs
- Linear algebra, probability theory, and general mathematical maturity.
- Programming skills (Python).
Information for registered students
For detailed lecture, tutorial and lab plans, and course material, please follow this link: https://liuonline-my.sharepoint.com/:o:/g/personal/erila39_liu_se/Eoj_ro-BBBRFj_Ob-osr-LgBMIh91qSP5xIDzDnqYuyI4w?e=ngQlTx