Contents
Contents
Tap any chapter to start reading.
Chapter 1 Basics of NetworksNodes, edges, adjacency matrices, paths, components. Construct and visualise graphs with NetworkX; compute degree, betweenness, closeness, and eigenvector centrality on real networks (Facebook ego graph, terrorist networks, trading networks).
Chapter 2 Identifying Important NodesPageRank as a random walk; influence maximization (greedy + submodularity); independent cascade and linear threshold models; strategic leaders in coordination games.
Chapter 3 Community DetectionModularity, Girvan–Newman edge betweenness, Louvain, Infomap. Choosing the right algorithm; evaluating partitions; interpreting communities in social, financial, and citation networks.
Chapter 4 Formation of NetworksErdős–Rényi random graphs, Watts–Strogatz small-world, Barabási–Albert preferential attachment. Why real networks are scale-free; diffusion of innovations on each model.
Chapter 5 Attention, Learning, and Games on NetworksBayesian and naïve social learning; DeGroot opinion dynamics; coordination, anti-coordination, and best-response games on networks; herding and information cascades.
How to read this book
Every Python code block in this book runs live in your browser. Click into any cell, edit it, press the ▶ Run button, and see the output. The Python engine (with NetworkX preloaded) downloads once on the first chapter — after that, everything is instant.
- Read in order — each chapter builds on graph primitives introduced in Chapter 1.
- Edit the code cells. Vary the seed, the network size, the parameters — that is how the intuition lands.
- Pair each chapter with the corresponding section of Jackson, Social and Economic Networks — the chapter numbers roughly match.
- If a derivation gets dense, scroll to the live code and re-derive what you just read by simulation.