Graph Machine Learning: Take graph data to the next level by applying machine learning techniques and algorithms

Build machine learning algorithms using graph data and efficiently exploit topological information within your models

Key Features:

• Implement machine learning techniques and algorithms in graph data
• Identify the relationship between nodes in order to make better business decisions
• Apply graph-based machine learning methods to solve real-life problems

Book Description:

Graph Machine Learning provides a new set of tools for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks.

You will start with a brief introduction to graph theory and graph machine learning, understanding their potential. As you proceed, you will become well versed with the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll then build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. Moving ahead, you will cover real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. Finally, you will learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, before progressing to explore the latest trends on graphs.

By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications.

What You Will Learn:

• Write Python scripts to extract features from graphs
• Distinguish between the main graph representation learning techniques
• Become well-versed with extracting data from social networks, financial transaction systems, and more
• Implement the main unsupervised and supervised graph embedding techniques
• Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more
• Deploy and scale out your application seamlessly

Who this book is for:

This book is for data analysts, graph developers, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance. The book will also be useful for data scientists and machine learning developers who want to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required. Intermediate-level working knowledge of Python programming and machine learning is also expected to make the most out of this book.

使用圖形數據構建機器學習算法，並高效地利用模型中的拓撲信息

主要特點：
- 在圖形數據中實現機器學習技術和算法
- 通過識別節點之間的關係來做出更好的業務決策
- 應用基於圖形的機器學習方法來解決現實問題

書籍描述：
《圖形機器學習》提供了一套新的工具，用於處理網絡數據並利用實體之間的關係來進行預測、建模和分析任務。

您將從簡要介紹圖論和圖形機器學習開始，了解它們的潛力。隨著學習的進展，您將熟悉用於圖形表示學習的主要機器學習模型：它們的目的、工作原理以及如何在各種監督和非監督學習應用中實施它們。然後，您將構建一個完整的機器學習流程，包括數據處理、模型訓練和預測，以充分利用圖形數據的潛力。接下來，您將涵蓋從社交網絡中提取數據、文本分析和自然語言處理（NLP）到使用圖形和金融交易系統的現實場景。最後，您將學習如何構建和擴展用於圖形分析的數據驅動應用程序，以存儲、查詢和處理網絡信息，然後探索圖形的最新趨勢。

通過閱讀本機器學習書籍，您將學習圖論的基本概念以及構建成功的機器學習應用所使用的所有算法和技術。

學到的內容：
- 使用Python腳本從圖形中提取特徵
- 區分主要的圖形表示學習技術
- 熟悉從社交網絡、金融交易系統等提取數據的方法
- 實施主要的非監督和監督圖形嵌入技術
- 掌握淺層嵌入方法、圖形神經網絡、圖形正則化方法等
- 無縫部署和擴展應用程序

適合閱讀對象：
本書適合數據分析師、圖形開發人員、圖形分析師和圖形專業人士，他們希望利用數據點之間的連接和關係中嵌入的信息來提升分析和模型性能。本書還對於希望建立以機器學習為驅動的圖形數據庫的數據科學家和機器學習開發人員也很有用。需要具備初級水平的圖形數據庫和圖形數據理解，以及中級水平的Python編程和機器學習知識，以充分利用本書的內容。