Supervised Graph Classification with Graph Embeddings

Graph-structured data is everywhere, from social networks to molecular structures, but classifying entire graphs presents unique computational challenges. This text-based course guides you through the process of transforming complex graph topologies into numerical vectors and applying supervised machine learning to classify them. What you'll learn: - Learn foundational graph theory terms, structures, and representation formats. - Generate graph-level embeddings that capture the global topology of a network. - Apply supervised machine learning models, including Support Vector Classifiers, to graph datasets. - Understand how to preprocess network data and handle common benchmark datasets in Python. - Implement clean, modern Python code for end-to-end graph classification workflows. The course starts with basic graph definitions and quickly moves into feature extraction, embedding generation, and hands-on classification pipelines with step-by-step code explanations. This course is designed for data scientists and developers looking to expand their machine learning toolkit into graph data, with no advanced graph theory background required. Begin reading today to unlock the power of graph-based machine learning.