Modern Graph Theory Algorithms with Python : Harness the power of graph algorithms and real-world network applications using Python

個数:

Modern Graph Theory Algorithms with Python : Harness the power of graph algorithms and real-world network applications using Python

  • 提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
    重要ご説明事項
    1. 納期遅延や、ご入手不能となる場合が若干ございます。
    2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
    3. 美品のご指定は承りかねます。

    ●3Dセキュア導入とクレジットカードによるお支払いについて
  • 【入荷遅延について】
    世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
    おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。
  • ◆画像の表紙や帯等は実物とは異なる場合があります。
  • ◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
    また、洋書販売価格は、ご注文確定時点での日本円価格となります。
    ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。
  • 製本 Paperback:紙装版/ペーパーバック版/ページ数 290 p.
  • 言語 ENG
  • 商品コード 9781805127895
  • DDC分類 511.5

Full Description

Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms

Key Features

Learn how to wrangle different types of datasets and analytics problems into networks
Leverage graph theoretic algorithms to analyze data efficiently
Apply the skills you gain to solve a variety of problems through case studies in Python
Purchase of the print or Kindle book includes a free PDF eBook

Book DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale.
This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You'll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you'll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you'll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter.
By the end of this book, you'll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learn

Transform different data types, such as spatial data, into network formats
Explore common network science tools in Python
Discover how geometry impacts spreading processes on networks
Implement machine learning algorithms on network data features
Build and query graph databases
Explore new frontiers in network science such as quantum algorithms

Who this book is forIf you're a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

Contents

Table of Contents

What is a Network?
Wrangling Data into Networks with NetworkX and igraph
Demographic Data
Transportation Data
Ecological Data
Stock Market Data
Goods Prices/Sales Data
Dynamic Social Networks
Machine Learning for Networks
Pathway Mining
Mapping Language Families - an Ontological Approach
Graph Databases
Putting It All Together
New Frontiers

最近チェックした商品