Description
Network systems ranging from social, technology to biology networks are ubiquitous. Examples of such networks include financial networks, social networks, robotics networks, biological networks, smart grids, communication networks and epidemic spread networks, etc. Network modelling and analysis of these systems are important in understanding behaviours these systems. This course aims to provide an introduction to various network models for modelling, analyzing and predicting properties of such networks, with the objective to equip students with the theoretical and numerical tools to study network systems. Students will be exposed to both theoretical and numerical tools in analyzing network systems. In addition, students will be trained in the reading and the implementation of results in seminal research papers. Topics in the course include: introduction to network systems, matrix theory and algebraic graph theory, centrality measures and applications, clustering for networks, consensus dynamics (including convergence analysis and applications), continous time positive systems, compartmental models, network synchronization and Kuramoto oscillators, epidemic spread over networks, network games and learning, as well as two special topics (one on graph neural networks, and the other on mean field games). This course is suitable for students who want to have some exposure of research topics related to network systems and games.