Graph Data Science with Neo4j: Learn how to use Neo4j 5 with Graph Data Science library 2.0 and its Python driver for your project (Paperback)

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 Description

Neo4j, 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 for

If 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.

超越極限，發揮 Neo4j 5 無限潛力，這是領先的圖形資料庫，適用於尖端機器學習。

購買印刷版或 Kindle 版本的書籍，即可免費獲得 PDF 電子書。

主要特點：

- 使用 Neo4j 最新版本 5 從圖形資料中提取有意義的資訊
- 在 Python 中將圖形演算法應用於常規機器學習流程
- 學習圖形資料科學庫的核心原則，進行預測並創建資料科學流程

書籍描述：

Neo4j 和其圖形資料科學（GDS）庫是一個完整的解決方案，用於存儲、查詢和分析圖形資料。隨著圖形資料庫在開發人員中越來越受歡迎，數據科學家在他們的職業生涯中可能會遇到這些資料庫，因此使用圖形演算法從中提取上下文資訊並提高整體模型預測性能成為一項不可或缺的技能。

使用 Python 的數據科學家將能夠通過這本實用指南將他們的知識應用於 Neo4j 和 GDS 库，該指南提供了基本概念的逐步解釋和實施使用最新的 Neo4j 版本 5 及其相關庫的圖形資料科學技術的實用指令。您將從使用 Cypher 查詢 Neo4j 開始，並學習如何描述圖形資料集。隨著您熟悉在 Neo4j 中運行圖形演算法，您將了解 GDS 库的新功能和高級功能，這些功能使您能夠進行預測並編寫資料科學流程。使用新發布的 GDSL Python 驅動程式，您將能夠將圖形演算法整合到 ML 流程中。

通過閱讀本書，您將能夠利用資料集中的關係來改進當前模型並進行其他複雜的預測。

您將學到什麼：

- 使用 Cypher 查詢語言查詢 Neo4j 等圖形資料庫
- 從自己的資料和公共知識圖形構建圖形資料集
- 進行特定於圖形的預測，如連結預測
- 探索最新版本的 Neo4j，構建圖形資料科學流程
- 使用圖形資料運行 scikit-learn 預測演算法
- 在 GDS 中訓練預測嵌入演算法並管理模型存儲庫

本書適合對 Neo4j 基礎知識有一定了解並希望了解如何構建高級分析解決方案的數據科學家或數據專業人士。熟悉 Python 和 Neo4j 的數據科學項目的主要組件對於理解本書中涵蓋的概念是必要的。

1. Introducing and Installing Neo4j
2. Using Existing Data to Build a Knowledge Graph
3. Characterizing a Graph Dataset
4. Using Graph Algorithms to Characterize a Graph Dataset
5. Visualizing Graph Data
6. Building a Machine Learning Model with Graph Features
7. Automatically Extracting Features with Graph Embeddings for Machine Learning
8. Building a GDS Pipeline for Node Classification Model Training
9. Predicting Future Edges
10. Writing Your Custom Graph Algorithm with the Pregel API

1. 介紹和安裝 Neo4j
2. 使用現有數據建立知識圖譜
3. 描述圖數據集
4. 使用圖算法描述圖數據集
5. 視覺化圖數據
6. 使用圖特徵建立機器學習模型
7. 使用圖嵌入自動提取機器學習特徵
8. 建立 GDS 管道進行節點分類模型訓練
9. 預測未來邊
10. 使用 Pregel API 編寫自定義圖算法