• Introduction to network data analysis. Basic concepts, representations of network data.
• Statistics for network analysis.
• Basic global and local properties (centralities, path-based properties)
• Basic global and local properties (structural properties)
• Network robustness
• Basic models - random graph, small world, preferential attachment
• Methods of network construction from vector data.
• Communities and network community structure
• Network models generating community structure
• Correlation in networks
• Sampling methods for network data
• Network visualization
Seminars follow the lectured topics and focus on solving practical tasks. Experiments are performed on small and medium-scale reference networks with prototyping implementations of selected methods and using tools and libraries (e.g., Gephi, libraries for R and Python).Introduction to network data analysis. Basic concepts, representations of network data.
• Statistics for network analysis.
• Basic global and local properties (centralities, path-based properties)
• Basic global and local properties (structural properties)
• Network robustness
• Basic models - random graph, small world, preferential attachment
• Methods of network construction from vector data.
• Communities and network community structure
• Network models generating community structure
• Correlation in networks
• Sampling methods for network data
• Network visualization
Seminars follow the lectured topics and focus on solving practical tasks. Experiments are performed on small and medium-scale reference networks with prototyping implementations of selected methods and using tools and libraries (e.g., Gephi, libraries for R and Python).Introduction to network data analysis. Basic concepts, representations of network data.