Connectomics, graph theory, and complexity

NOTE: This page is unfinished, use the Evernote instead

Graph theory is a branch of mathematics that can be used to understand connectomes. This note provides references on the basic principles of connectomics as well as graph theory.

Graph theory and network science

Online courses

• Introduction to Complexity (Santa Fe Institute 2015)
• Interactive complexity explainer (2019)
• Probabilistic graph models (Stanford 2023)
• Chaos and nonlinear dynamics lectures (Steven Strogatz 2014)
  • Video series from based on Strogatz's book (2021)
• Principles of complex systems (2023)
• History of complexity and network science video
• Dynamical systems in neuroscience video series (2021)
• Differential equations and dynamical systems video series (2022)
• Computational neuroscience animation channel
• Theoretical physics lecture notes and problems (Cambridge 2013)
• Graphs and networks resource set (2017)
• Brain networks (Russ Poldrack 2018)
• Logistic maps, fractals, and chaos video (Veritasium 2020)
• Collection of explorable explainers
  • Complexity explorables
  • Biological neural networks (2018)
  • Attractor landscapes (2018)
  • Thinking in systems (2017)
  • Watts-Strogatz small-world networks (2011)
  • Brain mapping (2019)
  • Graph theory (2017)
• Network neuroscience
• Information theory and self-organisation lectures (2020)
• Chaos lectures (2017)
• Network diffusion (via the graph Laplacian) explainer (2021)

Textbooks

• Fornito, Zalesky, Bullmore - Fundamentals of Brain Network Analysis (2016)
• Albert-László Barabási - Network Science (2015)
• Mark Newman - Networks (2018)
• Michele Coscia - The Atlas for the Aspiring Network Scientist (2025)
• Parr, Pezzulo, Friston - Active Inference (2022)
  • Walkthrough videos (2023)
• Porter and Gleeson - Dynamical Systems on Networks (2016)
• Newman and Barabási - The Structure and Dynamics of Networks (2006)
• Easley and Kleinberg - Networks, Crowds, and Markets (2010)
• Wasserman and Faust - Social Network Analysis (1994)
• Béla Bollobás - Random Graphs (2011)
• Eugene Izhikevich - Dynamical Systems in Neuroscience (2006)