Analyzing Social and Economic Networks

How do ideas spread through a community, and why do certain financial markets collapse while others remain resilient? The answers lie in the complex web of connections that shape our social and economic lives. This course provides a comprehensive introduction to the mathematical, sociological, and economic models used to analyze network structures. You will transition from understanding basic graph theory to analyzing real-world phenomena like information cascades, epidemic spreading, and social influence using modern analytical frameworks. What you'll learn: - Understand the foundational metrics of network analysis, including centrality, clustering, and degree distributions. - Model network formation using random graph models, strategic game-theoretic approaches, and preferential attachment. - Analyze how behaviors, technologies, and information diffuse through social and economic structures. - Evaluate the impact of network structures on economic decisions, learning, and collective human behavior. - Apply modern network analysis concepts to contemporary challenges like misinformation containment and algorithmic influence. You will begin with the core terminology and mathematical foundations of graph theory before exploring network formation models. From there, the written lessons guide you through diffusion processes, learning in networks, and practical economic applications. This course is designed for beginners in economics, sociology, data science, or applied mathematics who want to understand network science without needing advanced prior knowledge. Start reading today to decode the hidden structures that influence our connected world.