Applied Deep Learning On Graphs
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Applied Deep Learning on Graphs
Author: Lakshya Khandelwal
language: en
Publisher: Packt Publishing Ltd
Release Date: 2024-12-27
Gain a deep understanding of applied deep learning on graphs from data, algorithm, and engineering viewpoints to construct enterprise-ready solutions using deep learning on graph data for wide range of domains Key Features Explore graph data in real-world systems and leverage graph learning for impactful business results Dive into popular and specialized deep neural architectures like graph convolutional and attention networks Learn how to build scalable and productionizable graph learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book Description With their combined expertise spanning cutting-edge AI product development at industry giants such as Walmart, Adobe, Samsung, and Arista Networks, Lakshya and Subhajoy provide real-world insights into the transformative world of graph neural networks (GNNs). This book demystifies GNNs, guiding you from foundational concepts to advanced techniques and real-world applications. You’ll see how graph data structures power today’s interconnected world, why specialized deep learning approaches are essential, and how to address challenges with existing methods. You’ll start by dissecting early graph representation techniques such as DeepWalk and node2vec. From there, the book takes you through popular GNN architectures, covering graph convolutional and attention networks, autoencoder models, LLMs, and technologies such as retrieval augmented generation on graph data. With a strong theoretical grounding, you’ll seamlessly navigate practical implementations, mastering the critical topics of scalability, interpretability, and application domains such as NLP, recommendations, and computer vision. By the end of this book, you’ll have mastered the underlying ideas and practical coding skills needed to innovate beyond current methods and gained strategic insights into the future of GNN technologies. What you will learn Discover how to extract business value through a graph-centric approach Develop a basic understanding of learning graph attributes using machine learning Identify the limitations of traditional deep learning with graph data and explore specialized graph-based architectures Understand industry applications of graph deep learning, including recommender systems and NLP Identify and overcome challenges in production such as scalability and interpretability Perform node classification and link prediction using PyTorch Geometric Who this book is for For data scientists, machine learning practitioners, researchers delving into graph-based data, and software engineers crafting graph-related applications, this book offers theoretical and practical guidance with real-world examples. A foundational grasp of ML concepts and Python is presumed.
Deep Learning on Graphs
A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Graph Machine Learning
Author: Claudio Stamile
language: en
Publisher: Packt Publishing Ltd
Release Date: 2021-06-25
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.