Book Image

Hands-On Graph Analytics with Neo4j

By : Estelle Scifo
Book Image

Hands-On Graph Analytics with Neo4j

By: Estelle Scifo

Overview of this book

Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data.
Table of Contents (18 chapters)
1
Section 1: Graph Modeling with Neo4j
5
Section 2: Graph Algorithms
10
Section 3: Machine Learning on Graphs
14
Section 4: Neo4j for Production

Going beyond Louvain for overlapping community detection

As all algorithms do, the Louvain algorithm has its limitations. Understanding them is very important, so we'll try to do that in this section. We will also tackle possible alternatives. Finally, we are also going to talk about some algorithms that allow a node to belong to more than one community.

A caveat of the Louvain algorithm

Like any other algorithm, the Louvain algorithm has some known drawbacks. The main one is the resolution limit.

Resolution limit

Consider the following graph, consisting of strongly connected blobs of seven nodes each, weakly connected to each other with a single edge:

Running community detection on this graph, you would expect each of the blobs to form a community. While this works well for the Louvain algorithm on small graphs, it is known to fail on larger graphs. For instance, when run on a graph with a structure similar to the one depicted in the preceding figure but with 100 blobs, the Louvain...