The first part of this book will introduce you to the fascinating world of graph algorithms and data science, equipping you with the fundamental knowledge to harness their potential and …

In it, you’ll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project.

Along the way, you will learn how to use Cypher query language to manipulate graph structure and extract valuable insights. Next, I will walk you through the typical graph algorithms like …

When dealing with large amounts of data, you can use graph algorithms to deliver insights or pinpoint interesting parts of the graph that you can further explore through visualizations.

Graph Algorithms for Data Science With examples in Neo4j Tomaž Bratanić To comment go to liveBook

In this exciting journey of exploring graph data science, you have witnessed the power of graphs, learned about graph algorithms, and discovered how to use them in various scenarios.

The illustrated path in Figure 2.1 takes a bottom-up approach, where you first learn to describe the data for your domain as a graph, encompassing modeling and constructing a graph. Next, …

Chapters 3 and 4 are designed to introduce you to Cypher query language and show you how to use it in exploratory graph analysis. In chapter 5, you will learn how to characterize a network …

Instead of adjusting graph algorithms to support multipartite networks (multiple node and relationship types), the general approach is to first project a monopartite network (single node …