Dynamic Complex Network

Posted on Wed 04 March 2020 in courses

Course Description

This course provides an in-depth exploration of complex dynamic networks, focusing on their structure, behavior, and applications across various domains. Students will study the mathematical foundations, analysis techniques, and real-world implications of network dynamics. By examining interconnected systems, ranging from social networks to biological and technological networks, students will gain the tools necessary to model, analyze, and optimize network-based systems in the real world.


Course Objectives

By the end of this course, students will: 1. Understand the fundamental concepts and theories of network science.

  1. Analyze and characterize the dynamics of complex networks, including robustness, scalability, and efficiency.

  2. Apply mathematical and computational models to study network behavior and emergent phenomena.

  3. Explore the role of networks in diverse applications, including communication systems, social systems, biology, and finance.

  4. Develop practical skills in modeling and simulating dynamic networks using advanced tools and frameworks.


Syllabus

Week 1-2: Fundamentals of Complex Networks

  • Introduction to network science
  • Graph theory basics: nodes, edges, adjacency matrices
  • Types of networks: random, small-world, scale-free, and multiplex

Week 3-4: Network Structure and Properties

  • Key network metrics: degree distribution, centrality, clustering coefficient
  • Community detection and modularity
  • Real-world network structures and their implications

Week 5-6: Dynamic Processes on Networks

  • Diffusion and spreading processes (e.g., information, diseases)
  • Synchronization and collective behavior
  • Contagion models in social and biological networks

Week 7-8: Robustness and Resilience of Networks

  • Vulnerability analysis and fault tolerance
  • Cascading failures in critical infrastructure networks
  • Designing robust and resilient networked systems

Week 9-10: Network Optimization and Control

  • Controllability of complex networks
  • Optimizing network flows and resource allocation
  • Applications in transportation and communication systems

Week 11-12: Advanced Topics in Network Science

  • Temporal networks and evolving structures
  • Multilayer and interconnected networks
  • Data-driven approaches to network analysis

Week 13: Applications of Complex Dynamic Networks

  • Social networks and influence propagation
  • Biological and ecological networks
  • Applications in finance, power grids, and smart cities

Week 14: Capstone Project and Review

  • Designing, simulating, and analyzing a complex network for a chosen domain
  • Final presentations and peer reviews

Course Assessment

  • Assignments (25%): Analytical exercises and computational tasks.
  • Papsr-based Exam (40%): Theoretical understanding of network concepts.
  • Capstone Project (25%): Team-based project involving real-world network analysis.
  • Participation (10%): Active engagement in discussions, workshops, and reviews.

Resources

  • Textbooks:
  • Networks: An Introduction by Mark Newman
  • Dynamical Processes on Complex Networks by Alain Barrat et al.
  • Online Platforms:
  • Software tools such as Gephi, NetworkX (Python), and Cytoscape for network modeling and analysis
  • Online datasets for real-world network examples
  • Research Papers:
  • Recent publications on network science and dynamic systems from top journals

Prerequisites

  • A basic understanding of graph theory and linear algebra.
  • Familiarity with programming and data analysis tools.

Contact Information

For inquiries, feel free to reach out via my webpage: www.m-zakeri.github.io.


Course history

Teaching assistant

I was teaching assistant of Dynamic Complex Network M.Sc. and Ph.D. course by Dr. Hossein Rahmani for one semester (Winter and spring 2020) at Iran University of Science and Technology. Our teaching materials during these two years are available to view and download.

Useful links