Analyzing Social Graphs with NetworkX and Python

Understanding how people, entities, and communities connect is key to solving complex social and organizational problems. By modeling these relationships as mathematical graphs, you can uncover hidden patterns, influential nodes, and community structures. This text-based course guides you through the process of building, weighting, and analyzing social graphs using Python and the NetworkX library. You will transition from understanding basic network terminology to designing structured network models that reflect real-world affinities. What you will learn: Understand the fundamental concepts of graph theory, including nodes, edges, and degrees; Build weighted social graphs using NetworkX to represent relationship strength and affinity; Apply Python type hints and modern data structures to write clean, maintainable network code; Analyze network properties to identify key influencers and tightly-knit subgroups; Practice structuring relational data from raw formats into network models. You will start by learning the core terminology of network science before moving on to practical coding exercises. The material progresses logically from creating simple nodes to calculating complex metrics on weighted community graphs. This course is designed for beginners who have a basic familiarity with Python and want to explore data analysis and network science without any prior graph-theory experience. Start reading today to unlock the power of network analysis and start mapping complex relationships.