Teaching

Northeastern University

Part-time Lecturer, 2021–Present

CNET 5050 — Fundamentals of Complex Networks

An interdisciplinary introduction to the science of complex networks and the starting point for students developing expertise in network science. The course covers the mathematical foundations of networks (graph theory) and common tools for describing and analyzing them. Topics include degree distributions, centrality measures, path lengths, clustering, homophily, and robustness. The course also examines evolving and growing networks, network null models, and applications across biology, medicine, sociology, technology, and finance. Students conduct their own analysis of a real network dataset as part of their final project.

PHYS 5116 / NETS 5116 — Network Science

Introduces network science and the set of analytical, numerical, and modeling tools used to understand complex networks emerging in nature and technology. Focuses on the empirical study of real networks, with examples coming from biology (metabolic, protein interaction networks), computer science (World Wide Web, Internet), or social systems (e-mail, friendship networks). Shows the organizing principles that govern the emergence of networks and the set of tools necessary to characterize and model them. Covers elements of graph theory, statistical physics, biology, and social science as they pertain to the understanding of complex systems.

Colegio de México

Postdoctoral Fellow, 2020–2021

Mathematics 1

A college-level introductory course in mathematics for social science students. Covers the foundations of probability, linear algebra, and calculus, equipping students with the quantitative tools needed for rigorous analysis in the social sciences.

Instituto Tecnológico Autónomo de México (ITAM)

Co-taught with Dra. Diana Terrazas Santamaría

Economía de Redes (Network Economics)

Network theory has gained significant traction over the past two decades as a framework for explaining economic phenomena, grounded in the idea that agents form strategic relationships for individual or collective benefit. The structure of a network affects the welfare of the agents involved—both individually and collectively—and leveraging the information a network provides requires theoretical tools to understand how connections between agents emerge and evolve.

Syllabus:

  1. Introduction
    • Introduction to Python
  2. Definitions and metrics
    • Introduction to networkx
    • Network analysis with networkx
  3. Random networks
    • The Barabási–Albert model
    • Random models in Python
  4. Strategic formation
  5. Diffusion in networks
    • Centrality measures in Python
  6. Applications
    • Economic networks
    • Technological networks
    • Social networks

Course materials (Jupyter notebooks): github.com/rodogi/clase_redes