Stamile, Claudio
Graph Machine Learning: Take Graph Data To The Next Level By Applying Machine Learning Techniques And Algorithms - United Kingdom -- Packt Publishing Limited -- 2021 - xi, 319p.
*Getting Started with Graphs
*Graph Machine Learning
*Machine Learning on Graphs
*Unsupervised Graph Learning
*Supervised Graph Learning
*Problems with Machine Learning on Graphs
*Social Network Graphs
*Text Analytics and Natural Language Processing Using Graphs
*Graph Analysis for Credit Card Transactions
*Building a Data-Driven Graph-Powered Application
*Novel Trends on Graphs
"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."--Description provided by publisher
9781800204492
Computer Science
Artificial intelligence
Graph theory Data processing
Machine learning
006.31 STA/G
Graph Machine Learning: Take Graph Data To The Next Level By Applying Machine Learning Techniques And Algorithms - United Kingdom -- Packt Publishing Limited -- 2021 - xi, 319p.
*Getting Started with Graphs
*Graph Machine Learning
*Machine Learning on Graphs
*Unsupervised Graph Learning
*Supervised Graph Learning
*Problems with Machine Learning on Graphs
*Social Network Graphs
*Text Analytics and Natural Language Processing Using Graphs
*Graph Analysis for Credit Card Transactions
*Building a Data-Driven Graph-Powered Application
*Novel Trends on Graphs
"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."--Description provided by publisher
9781800204492
Computer Science
Artificial intelligence
Graph theory Data processing
Machine learning
006.31 STA/G