Introduction to Network Science

Knowledge

Basic knowledge of probability, statistics, linear algebra, and programming in Python

Description

This course provides a self-contained introduction to the interdisciplinary field of network science, which explores the structure, dynamics, and function of complex networks found in nature, society, technology, and information systems.
Students learn the foundational principles of graph theory, network modeling, and the analysis of real-world networks such as social networks, biological networks, and communication systems.

Learning objectives

After successfully completing the course, students will be able to

•    Demonstrate a quantitative intuition about networks and ability to reason about network phenomena
•    Explain network representations
•    Summarize the principles and methods for describing network data
•    Conduct and interpret numerical network experiments
•    Analyze and model real-world network data 

Comment

Content:

•    Mathematical models of network structure
•    Network formation mechanisms
•    Descriptive network analysis
•    Mixing patterns and community structure
•    Dynamical processes on networks
•    Spreading dynamics and percolation
•    Diffusion models
•    Opinion formation and cascades
•    Networks in the real world
•    Classes of empirical networks
•    Network data sources, acquisition, and error
assessment
•    Network inference