Graph Algorithms for Data Science: With Examples in Neo4j
Bratanic, Tomaz
- 出版商: Manning
- 出版日期: 2024-02-27
- 售價: $2,300
- 貴賓價: 9.5 折 $2,185
- 語言: 英文
- 頁數: 352
- 裝訂: Quality Paper - also called trade paper
- ISBN: 1617299464
- ISBN-13: 9781617299469
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相關分類:
NoSQL、Algorithms-data-structures、Data Science
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相關主題
商品描述
Practical methods for analyzing your data with graphs, revealing hidden connections and new insights. Graphs are the natural way to represent and understand connected data. This book explores the most important algorithms and techniques for graphs in data science, with concrete advice on implementation and deployment. You don't need any graph experience to start benefiting from this insightful guide. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. In Graph Algorithms for Data Science you will learn:
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you'll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside
About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaz Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS
1 Graphs and network science: An introduction
2 Representing network structure: Designing your first graph model
PART 2 SOCIAL NETWORK ANALYSIS
3 Your first steps with Cypher query language
4 Exploratory graph analysis
5 Introduction to social network analysis
6 Projecting monopartite networks
7 Inferring co-occurrence networks based on bipartite networks
8 Constructing a nearest neighbor similarity network
PART 3 GRAPH MACHINE LEARNING
9 Node embeddings and classification
10 Link prediction
11 Knowledge graph completion
12 Constructing a graph using natural language processing technique
- Labeled-property graph modeling
- Constructing a graph from structured data such as CSV or SQL
- NLP techniques to construct a graph from unstructured data
- Cypher query language syntax to manipulate data and extract insights
- Social network analysis algorithms like PageRank and community detection
- How to translate graph structure to a ML model input with node embedding models
- Using graph features in node classification and link prediction workflows
Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. Foreword by Michael Hunger. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A graph, put simply, is a network of connected data. Graphs are an efficient way to identify and explore the significant relationships naturally occurring within a dataset. This book presents the most important algorithms for graph data science with examples from machine learning, business applications, natural language processing, and more. About the book Graph Algorithms for Data Science shows you how to construct and analyze graphs from structured and unstructured data. In it, you'll learn to apply graph algorithms like PageRank, community detection/clustering, and knowledge graph models by putting each new algorithm to work in a hands-on data project. This cutting-edge book also demonstrates how you can create graphs that optimize input for AI models using node embedding. What's inside
- Creating knowledge graphs
- Node classification and link prediction workflows
- NLP techniques for graph construction
About the reader For data scientists who know machine learning basics. Examples use the Cypher query language, which is explained in the book. About the author Tomaz Bratanic works at the intersection of graphs and machine learning. Arturo Geigel was the technical editor for this book. Table of Contents PART 1 INTRODUCTION TO GRAPHS
1 Graphs and network science: An introduction
2 Representing network structure: Designing your first graph model
PART 2 SOCIAL NETWORK ANALYSIS
3 Your first steps with Cypher query language
4 Exploratory graph analysis
5 Introduction to social network analysis
6 Projecting monopartite networks
7 Inferring co-occurrence networks based on bipartite networks
8 Constructing a nearest neighbor similarity network
PART 3 GRAPH MACHINE LEARNING
9 Node embeddings and classification
10 Link prediction
11 Knowledge graph completion
12 Constructing a graph using natural language processing technique
商品描述(中文翻譯)
使用圖形進行數據分析的實用方法，揭示隱藏的關聯和新見解。
圖形是表示和理解連接數據的自然方式。本書探討了數據科學中最重要的圖形算法和技術，並提供了有關實施和部署的具體建議。您無需具備任何圖形經驗即可從這本富有洞察力的指南中受益。這些強大的圖形算法通過清晰、不帶行話的文本和插圖加以解釋，使它們易於應用於您自己的項目中。 在《數據科學的圖形算法》中，您將學到:- 帶標籤屬性的圖形建模
- 從結構化數據（如CSV或SQL）構建圖形
- 使用自然語言處理技術從非結構化數據構建圖形
- 使用Cypher查詢語言語法操作數據並提取見解
- 社交網絡分析算法，如PageRank和社區檢測
- 如何將圖形結構轉化為具有節點嵌入模型的機器學習模型輸入
- 在節點分類和鏈接預測工作流程中使用圖形特徵
《數據科學的圖形算法》是一本關於在機器學習、欺詐檢測和業務數據分析等應用中使用基於圖形的數據的實用指南。它充滿了迷人而有趣的項目，展示了圖形的內幕。通過分析Twitter、使用自然語言處理技術構建圖形等方式，您將獲得實用技能。 Michael Hunger作序。 購買印刷版書籍可獲得Manning Publications的PDF、Kindle和ePub格式的免費電子書。 關於技術 簡單地說，圖形是一個連接數據的網絡。圖形是識別和探索數據集中自然發生的重要關係的有效方式。本書介紹了圖形數據科學中最重要的算法，並提供了機器學習、業務應用、自然語言處理等方面的示例。 關於本書 《數據科學的圖形算法》向您展示如何從結構化和非結構化數據中構建和分析圖形。在這本書中，您將學習如何將圖形算法（如PageRank、社區檢測/聚類和知識圖模型）應用於實際的數據項目中。這本尖端的書籍還演示了如何使用節點嵌入創建優化AI模型輸入的圖形。 內容簡介
- 創建知識圖
- 節點分類和鏈接預測工作流程
- 用於圖形構建的自然語言處理技術
讀者對象 對機器學習基礎知識有所了解的數據科學家。書中解釋了Cypher查詢語言。 關於作者 Tomaz Bratanic在圖形和機器學習的交叉領域工作。 Arturo Geigel是本書的技術編輯。 目錄 第1部分 圖形介紹
1 圖形和網絡科學：介紹
2 表示網絡結構：設計第一個圖形模型
第2部分 社交網絡分析
3 使用Cypher查詢語言的第一步
4 探索性圖形分析
5 社交網絡分析簡介
6 基於單一類別網絡的投影
7 基於雙分類網絡推斷共現網絡
8 構建最近鄰相似性網絡
第3部分 圖形機器學習
9 節點嵌入和分類
10 鏈接預測
11 知識圖完成
12 使用自然語言處理技術構建圖形
作者簡介
Tomaz Bratanic is a network scientist at heart, working at the intersection of graphs and machine learning. He has applied these graph techniques to projects in various domains including fraud detection, biomedicine, business-oriented analytics, and recommendations.
作者簡介(中文翻譯)
Tomaz Bratanic是一位熱愛網絡科學的專家，致力於圖形和機器學習的交叉領域研究。他將這些圖形技術應用於各個領域的專案，包括詐騙檢測、生物醫學、商業分析和推薦系統。
