Unsupervised Graph Clustering and Community Detection

Network data is everywhere, from social connections to biological pathways, but extracting meaningful patterns from these structures requires specialized techniques. This written guide introduces you to the core concepts of graph theory and unsupervised machine learning, showing you how to find hidden structures without manual labeling. Through clear explanations and practical code walkthroughs, you will transition from a graph beginner to confidently partitioning networks, finding cohesive groups, and generating node embeddings. What you'll learn: - Understand foundational graph concepts, including nodes, edges, adjacency matrices, and network properties; - Apply classical community detection algorithms such as label propagation and Louvain modularity optimization; - Implement representation learning techniques like DeepWalk to generate low-dimensional node embeddings; - Evaluate clustering quality using modern metrics such as modularity, conductance, and normalized mutual information; - Analyze real-world network datasets to uncover hidden structures and relationships. The course starts with essential graph terminology and basic structural metrics before moving step-by-step through algorithmic approaches, embedding techniques, and practical evaluation methods. It is designed for aspiring data scientists, analysts, and programmers who want to learn network analysis from scratch, with no prior graph theory experience required. Dive in and start uncovering the hidden communities within your network data today.