Neo4j Graph Data Science: Topological Link Prediction

Discovering hidden relationships and forecasting new connections is one of the most powerful applications of graph analytics. This text-based guide introduces you to topological link prediction using the Neo4j Graph Data Science (GDS) library, giving you the practical tools needed to analyze connected networks effectively. You will build a solid theoretical foundation in graph topology before applying standard scoring algorithms directly through Cypher queries. By exploring structural proximity measures, you will learn how to extract meaningful network features and evaluate relationship likelihood between nodes. What you'll learn: - Understand core graph concepts, node properties, and in-memory graph projections in Neo4j. - Calculate local topological metrics including Common Neighbors, Jaccard Similarity, and Preferential Attachment. - Apply advanced proximity algorithms such as Adamic-Adar and Resource Allocation to identify strong connection candidates. - Extract topological scores as engineered features for broader graph machine learning pipelines. - Query and analyze link prediction scores using practical Cypher script examples. The course begins with foundational graph terminology and setup procedures before progressing into algorithm execution and feature extraction. Designed for data analysts, developers, and beginners to graph algorithms, no prior advanced graph theory background is required. Begin reading today to uncover meaningful connections within your network data.