A Practical Introduction to Graph Algorithms

Summary

Most real-world data problems aren’t about rows and columns — they’re about relationships.

Whether you’re trying to recommend the right product, uncover hidden customer segments, identify critical dependencies, or find the fastest path through a complex system, the answer depends on how things connect. Graph algorithms are built for exactly these problems.

This guide shows you how to solve them by developing intuition for a small, practical set of graph algorithms. Instead of treating algorithms as black boxes, you’ll learn how they work, what signal each one extracts from a network, and how to reason about their results.

Read this guide to learn how five core graph algorithms solve 80% of real-world graph analytics problems.

• Build intuition for how algorithms like Jaccard, Louvain, PageRank, Dijkstra, and FastRP work under the hood

• Understand when to use each algorithm, what assumptions it makes, and how to interpret its output

• Connect algorithm behavior to real use cases like recommendations, segmentation, risk analysis, and optimization

By the end, you’ll be able to get the most out of Neo4j Graph Analytics — choosing the right algorithm with confidence, explaining its results clearly, and applying it effectively to your own data.