Graph Machine Learning

Graph Machine Learning
Author :
Publisher : Packt Publishing Ltd
Total Pages : 338
Release :
ISBN-10 : 9781800206755
ISBN-13 : 1800206755
Rating : 4/5 (755 Downloads)

Book Synopsis Graph Machine Learning by : Claudio Stamile

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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 will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about 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 build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through 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. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and 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 Learn how to extract data from social networks, financial transaction systems, for text analysis, 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 scientists, data analysts, 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 using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.


Graph Machine Learning Related Books

Graph Machine Learning
Language: en
Pages: 338
Authors: Claudio Stamile
Categories: Computers
Type: BOOK - Published: 2021-06-25 - Publisher: Packt Publishing Ltd

DOWNLOAD EBOOK

Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning te
Graph Representation Learning
Language: en
Pages: 141
Authors: William L. William L. Hamilton
Categories: Computers
Type: BOOK - Published: 2022-06-01 - Publisher: Springer Nature

DOWNLOAD EBOOK

Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational induct
Graph-Powered Machine Learning
Language: en
Pages: 494
Authors: Alessandro Negro
Categories: Computers
Type: BOOK - Published: 2021-10-05 - Publisher: Simon and Schuster

DOWNLOAD EBOOK

Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learn
Deep Learning on Graphs
Language: en
Pages: 339
Authors: Yao Ma
Categories: Computers
Type: BOOK - Published: 2021-09-23 - Publisher: Cambridge University Press

DOWNLOAD EBOOK

A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Graph Neural Networks: Foundations, Frontiers, and Applications
Language: en
Pages: 701
Authors: Lingfei Wu
Categories: Computers
Type: BOOK - Published: 2022-01-03 - Publisher: Springer Nature

DOWNLOAD EBOOK

Deep Learning models are at the core of artificial intelligence research today. It is well known that deep learning techniques are disruptive for Euclidean data