Social Network Graphs and Link Prediction for Beginners

Understanding how connections form in social networks is key to modern recommendation systems and fraud detection. This text-based course guides you through the fundamentals of network science and graph machine learning without requiring an advanced mathematical background. You will transition from understanding basic graph theory to generating synthetic social networks and setting up machine learning pipelines to predict future connections. What you will learn: 1. Understand foundational graph theory concepts, including nodes, edges, degree distribution, and clustering coefficients. 2. Generate synthetic small-world networks using the Watts-Strogatz model in Python. 3. Prepare and preprocess graph data, splitting networks into training and testing sets for machine learning. 4. Extract topological features, such as Jaccard coefficient and preferential attachment, to feed predictive models. 5. Apply modern link prediction techniques using Python libraries like NetworkX and basic machine learning classifiers. 6. Explore modern trends in graph machine learning, including an introduction to node embeddings and Graph Neural Networks. You will start with essential definitions of graph structures before moving step-by-step through synthetic graph generation, feature engineering, and hands-on dataset preparation for machine learning algorithms. This course is designed for aspiring data scientists, analysts, and developers who are new to network analysis and want a clear, code-supported introduction to graph-based machine learning. No prior experience with graph theory is required. Start reading today to build and analyze your first social network graphs.