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Supercharge your data with the limitless potential of Neo4j 5, the
premier graph database for cutting-edge machine learning Purchase
of the print or Kindle book includes a free PDF eBook Key Features
Extract meaningful information from graph data with Neo4j's latest
version 5 Use Graph Algorithms into a regular Machine Learning
pipeline in Python Learn the core principles of the Graph Data
Science Library to make predictions and create data science
pipelines. Book DescriptionNeo4j, along with its Graph Data Science
(GDS) library, is a complete solution to store, query, and analyze
graph data. As graph databases are getting more popular among
developers, data scientists are likely to face such databases in
their career, making it an indispensable skill to work with graph
algorithms for extracting context information and improving the
overall model prediction performance. Data scientists working with
Python will be able to put their knowledge to work with this
practical guide to Neo4j and the GDS library that offers
step-by-step explanations of essential concepts and practical
instructions for implementing data science techniques on graph data
using the latest Neo4j version 5 and its associated libraries.
You’ll start by querying Neo4j with Cypher and learn how to
characterize graph datasets. As you get the hang of running graph
algorithms on graph data stored into Neo4j, you’ll understand the
new and advanced capabilities of the GDS library that enable you to
make predictions and write data science pipelines. Using the newly
released GDSL Python driver, you’ll be able to integrate graph
algorithms into your ML pipeline. By the end of this book, you’ll
be able to take advantage of the relationships in your dataset to
improve your current model and make other types of elaborate
predictions. What you will learn Use the Cypher query language to
query graph databases such as Neo4j Build graph datasets from your
own data and public knowledge graphs Make graph-specific
predictions such as link prediction Explore the latest version of
Neo4j to build a graph data science pipeline Run a scikit-learn
prediction algorithm with graph data Train a predictive embedding
algorithm in GDS and manage the model store Who this book is forIf
you’re a data scientist or data professional with a foundation in
the basics of Neo4j and are now ready to understand how to build
advanced analytics solutions, you’ll find this graph data science
book useful. Familiarity with the major components of a data
science project in Python and Neo4j is necessary to follow the
concepts covered in this book.
Discover how to use Neo4j to identify relationships within complex
and large graph datasets using graph modeling, graph algorithms,
and machine learning Key Features Get up and running with graph
analytics with the help of real-world examples Explore various use
cases such as fraud detection, graph-based search, and
recommendation systems Get to grips with the Graph Data Science
library with the help of examples, and use Neo4j in the cloud for
effective application scaling Book DescriptionNeo4j 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. What you will learn Become well-versed with Neo4j graph
database building blocks, nodes, and relationships Discover how to
create, update, and delete nodes and relationships using Cypher
querying Use graphs to improve web search and recommendations
Understand graph algorithms such as pathfinding, spatial search,
centrality, and community detection Find out different steps to
integrate graphs in a normal machine learning pipeline Formulate a
link prediction problem in the context of machine learning
Implement graph embedding algorithms such as DeepWalk, and use them
in Neo4j graphs Who this book is forThis book is for data analysts,
business analysts, graph analysts, and database developers looking
to store and process graph data to reveal key data insights. This
book will also appeal to data scientists who want to build
intelligent graph applications catering to different domains. Some
experience with Neo4j is required.
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