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.

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
  

Course syllabus, schedule, lecture and tutorial plan, and more information: please follow this link.

Page responsible: Erik G. Larsson
Last updated: 2020 11 02   14:52