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.

Course topics

• 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

Instructors

• Course director and lecturer: Danyo Danev
• Assistants: David Nordlund, Daniel Perez Herrera, Ahmet Kaplan, Jianan Bai and Sai Thoota

Course material

• 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

Prerequisites

• 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.sharepoint.com/sites/Lisam_TSKS33_2023HT_CU/