Applied Deep Learning on Graphs

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.
Table of Contents
Introduction to Graph Learning
Graph Learning in the Real World
Graph Representation Learning
Deep Learning Models for Graphs
Graph Deep Learning Challenges
Harnessing Large Language Models for Graph Learning
Graph Deep Learning in Practice
Graph Deep Learning for Natural Language Processing
Building Recommendation Systems Using Graph Deep Learning
Graph Deep Learning for Computer Vision
Emerging Applications
The Future of Graph Learning

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