Graph Data Modeling in Python: A practical guide to curating, analyzing, and modeling data with graphs

Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Transform relational data models into graph data model while learning key applications along the way
• Discover common challenges in graph modeling and analysis, and learn how to overcome them
• Practice real-world use cases of community detection, knowledge graph, and recommendation network

Book Description:

Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis.

Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements.

By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time.

What You Will Learn:

• Design graph data models and master schema design best practices
• Work with the NetworkX and igraph frameworks in Python
• Store, query, ingest, and refactor graph data
• Store your graphs in memory with Neo4j
• Build and work with projections and put them into practice
• Refactor schemas and learn tactics for managing an evolved graph data model

Who this book is for:

If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.

學習如何使用Python程式語言進行轉換、儲存、演化、重構、建模和創建圖形投影。

購買印刷版或Kindle電子書，即可免費獲得PDF電子書。

主要特點：
- 在學習過程中，將關聯數據模型轉換為圖形數據模型
- 探索圖形建模和分析中的常見挑戰，並學習如何克服它們
- 實踐社區檢測、知識圖和推薦網絡的實際應用案例

書籍描述：
圖形在我們日常生活中的產品和服務中變得越來越重要，推動社交媒體、網上購物推薦甚至是詐騙檢測。通過本書，您將了解到良好的圖形數據模型如何通過複雜的網絡分析來提高效率並揭示隱藏的洞察力。

《Python圖形數據建模》將指導您使用流行的開源Python庫NetworkX和igraph設計、實施和利用各種圖形數據模型。通過實際的使用案例和示例，您將了解如何設計能夠支持各種查詢和功能的最佳圖形模型。此外，您還將無縫過渡從傳統的關聯數據庫和表格數據到動態的圖形數據結構，這些結構允許強大的基於路徑的分析。除了學習如何使用Neo4j管理持久性圖形數據庫外，您還將掌握如何根據不斷變化的數據需求調整您的網絡模型。

通過閱讀本書，您將能夠將表格數據轉換為強大的圖形數據模型。總之，您將能夠在短時間內從初學者發展為高級實踐者。

學到的內容：
- 設計圖形數據模型並掌握模式設計的最佳實踐
- 在Python中使用NetworkX和igraph框架
- 儲存、查詢、輸入和重構圖形數據
- 使用Neo4j在內存中儲存圖形
- 建立和使用投影並將其應用到實踐中
- 重構模式並學習管理演化的圖形數據模型的策略

本書適合對學習圖形數據庫以及如何整理和提取數據感興趣的數據分析師或數據庫開發人員。對於希望開始進行圖形數據建模的數據科學家和Python開發人員也很有益。雖然假設讀者具有Python知識，但不需要先前的圖形數據建模理論和技術經驗。