Graph Machine Learning
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Graph Representation Learning
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Author : William L. Hamilton
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2020-09-16
Graph Representation Learning written by William L. Hamilton and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-09-16 with Computers categories.
This book is a foundational guide to graph representation learning, including state-of-the art advances, and introduces the highly successful graph neural network (GNN) formalism. Graph-structured data is ubiquitous throughout the natural and social sciences, from telecommunication networks to quantum chemistry. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from this kind of data. Recent years have seen a surge in research on graph representation learning, including techniques for deep graph embeddings, generalizations of convolutional neural networks to graph-structured data, and neural message-passing approaches inspired by belief propagation. These advances in graph representation learning have led to new state-of-the-art results in numerous domains, including chemical synthesis, 3D vision, recommender systems, question answering, and social network analysis. It begins with a discussion of the goals of graph representation learning as well as key methodological foundations in graph theory and network analysis. Following this, the book introduces and reviews methods for learning node embeddings, including random-walk-based methods and applications to knowledge graphs. It then provides a technical synthesis and introduction to the highly successful graph neural network (GNN) formalism, which has become a dominant and fast-growing paradigm for deep learning with graph data. The book concludes with a synthesis of recent advancements in deep generative models for graphs -- a nascent but quickly growing subset of graph representation learning.
Graph Machine Learning
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Author : Claudio Stamile
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-06-25
Graph Machine Learning written by Claudio Stamile and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.
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.
Introduction To Graph Neural Networks
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Author : Zhiyuan Liu
language : en
Publisher: Springer Nature
Release Date : 2022-05-31
Introduction To Graph Neural Networks written by Zhiyuan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-05-31 with Computers categories.
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed to combine the feature information and the graph structure to learn better representations on graphs via feature propagation and aggregation. Due to its convincing performance and high interpretability, GNN has recently become a widely applied graph analysis tool. This book provides a comprehensive introduction to the basic concepts, models, and applications of graph neural networks. It starts with the introduction of the vanilla GNN model. Then several variants of the vanilla model are introduced such as graph convolutional networks, graph recurrent networks, graph attention networks, graph residual networks, and several general frameworks. Variants for different graph types and advanced training methods are also included. As for the applications of GNNs, the book categorizes them into structural, non-structural, and other scenarios, and then it introduces several typical models on solving these tasks. Finally, the closing chapters provide GNN open resources and the outlook of several future directions.
Graph Neural Networks Foundations Frontiers And Applications
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Author : Lingfei Wu
language : en
Publisher: Springer Nature
Release Date : 2022-01-03
Graph Neural Networks Foundations Frontiers And Applications written by Lingfei Wu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-03 with Computers categories.
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, such as images or sequence data, and not immediately applicable to graph-structured data such as text. This gap has driven a wave of research for deep learning on graphs, including graph representation learning, graph generation, and graph classification. The new neural network architectures on graph-structured data (graph neural networks, GNNs in short) have performed remarkably on these tasks, demonstrated by applications in social networks, bioinformatics, and medical informatics. Despite these successes, GNNs still face many challenges ranging from the foundational methodologies to the theoretical understandings of the power of the graph representation learning. This book provides a comprehensive introduction of GNNs. It first discusses the goals of graph representation learning and then reviews the history, current developments, and future directions of GNNs. The second part presents and reviews fundamental methods and theories concerning GNNs while the third part describes various frontiers that are built on the GNNs. The book concludes with an overview of recent developments in a number of applications using GNNs. This book is suitable for a wide audience including undergraduate and graduate students, postdoctoral researchers, professors and lecturers, as well as industrial and government practitioners who are new to this area or who already have some basic background but want to learn more about advanced and promising techniques and applications.
Graph Machine Learning Mastery
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Author : Philip Oscar
language : en
Publisher: Independently Published
Release Date : 2025-12-17
Graph Machine Learning Mastery written by Philip Oscar and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-17 with Computers categories.
Graph Machine Learning Mastery A Complete Guide to Graph Neural Networks, Graph Transformers, Temporal GNNs, and LLM-Powered Graph AI with PyTorch Geometric & DGL Graph-structured data powers today's most advanced AI systems-from recommendation engines and fraud detection to drug discovery, cybersecurity, and large-scale knowledge graphs. Graph Machine Learning Mastery is the definitive, end-to-end guide for engineers, researchers, and data scientists who want to design, train, scale, and deploy production-ready graph AI systems using state-of-the-art techniques. This book goes far beyond theory. You'll master Graph Neural Networks (GNNs), Graph Transformers, Temporal & Dynamic Graph Models, and LLM-augmented Graph AI, all with hands-on implementations using industry-standard frameworks like and . What You'll Learn Build powerful GNN architectures: GCN, GAT, GraphSAGE, GIN, heterogeneous and large-scale GNNs Transition from GNNs to Graph Transformers with positional encodings and attention mechanisms Model temporal and dynamic graphs using TGN, TGAT, DySAT, and continuous-time message passing Design LLM + GNN hybrid systems for reasoning, knowledge graphs, and GraphRAG pipelines Apply graph ML to real-world domains: fraud detection, recommender systems, molecular graphs, finance, telecom, and cybersecurity Train, optimize, monitor, and deploy graph models in production environments Integrate GNNs with graph databases, MLOps pipelines, and scalable inference system. Hands-On, End-to-End Projects You'll implement complete production-grade projects including: Node classification, graph classification, and link prediction Temporal graph forecasting Molecular property prediction with OGB benchmarks Graph-augmented LLM systems for intelligent reasoning and recommendation. Each project walks you through data preprocessing, model architecture, training, evaluation, deployment, and monitoring-so you don't just learn concepts, you build real systems. Who This Book Is For Data scientists and ML engineers expanding into graph-based AI AI researchers exploring next-generation GNN and Transformer architectures Backend and platform engineers deploying graph intelligence at scale Professionals working with knowledge graphs, recommendation systems, and complex networks A working knowledge of Python and basic machine learning is recommended. Why This Book Stands Out Unlike fragmented tutorials or outdated references, Graph Machine Learning Mastery delivers a modern, unified, and production-focused roadmap-from classical graph learning to cutting-edge LLM-powered Graph AI. With deep technical insight, real-world case studies, and extensive appendices packed with APIs, cheat sheets, troubleshooting guides, and learning paths, this book is designed to become your long-term reference and career accelerator. If you're serious about mastering Graph Machine Learning, Graph Transformers, Temporal GNNs, and LLM-driven AI systems, this is the book you've been waiting for.
Deep Learning On Graphs
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Author : Yao Ma
language : en
Publisher: Cambridge University Press
Release Date : 2021-09-23
Deep Learning On Graphs written by Yao Ma and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-23 with Computers categories.
A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
Graph Neural Networks In Action
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Author : Keita Broadwater
language : en
Publisher: Simon and Schuster
Release Date : 2025-04-15
Graph Neural Networks In Action written by Keita Broadwater and has been published by Simon and Schuster this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-15 with Computers categories.
Graph Neural Networks in Action is a great guide about how to build cutting-edge graph neural networks and powerful deep learning models for recommendation engines, molecular modeling, and more. Ideal for Python programmers, you will dive into graph neural networks perfect for node prediction, link prediction, and graph classification.
Scaling Graph Learning For The Enterprise
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Author : Ahmed Menshawy
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2025-08-06
Scaling Graph Learning For The Enterprise written by Ahmed Menshawy and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-06 with Computers categories.
Tackle the core challenges related to enterprise-ready graph representation and learning. With this hands-on guide, applied data scientists, machine learning engineers, and practitioners will learn how to build an E2E graph learning pipeline. You'll explore core challenges at each pipeline stage, from data acquisition and representation to real-time inference and feedback loop retraining. Drawing on their experience building scalable and production-ready graph learning pipelines, the authors take you through the process of building robust graph learning systems in a world of dynamic and evolving graphs. Understand the importance of graph learning for boosting enterprise-grade applications Navigate the challenges surrounding the development and deployment of enterprise-ready graph learning and inference pipelines Use traditional and advanced graph learning techniques to tackle graph use cases Use and contribute to PyGraf, an open source graph learning library, to help embed best practices while building graph applications Design and implement a graph learning algorithm using publicly available and syntactic data Apply privacy-preserving techniques to the graph learning process
Graph Machine Learning
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Author : Aldo Marzullo
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-07-18
Graph Machine Learning written by Aldo Marzullo and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Mathematics categories.
Enhance your data science skills with this updated edition featuring new chapters on LLMs, temporal graphs, and updated examples with modern frameworks, including PyTorch Geometric and DGL Free with your book: DRM-free PDF version + access to Packt's next-gen Reader* Key Features Master new graph ML techniques through updated examples using PyTorch Geometric and Deep Graph Library (DGL) Explore GML frameworks and their main characteristics Leverage LLMs for machine learning on graphs and learn about temporal learning Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionGraph Machine Learning, Second Edition builds on its predecessor’s success, delivering the latest tools and techniques for this rapidly evolving field. From basic graph theory to advanced ML models, you’ll learn how to represent data as graphs to uncover hidden patterns and relationships, with practical implementation emphasized through refreshed code examples. This thoroughly updated edition replaces outdated examples with modern alternatives such as PyTorch and DGL, available on GitHub to support enhanced learning. The book also introduces new chapters on large language models and temporal graph learning, along with deeper insights into modern graph ML frameworks. Rather than serving as a step-by-step tutorial, it focuses on equipping you with fundamental problem-solving approaches that remain valuable even as specific technologies evolve. You will have a clear framework for assessing and selecting the right tools. By the end of this book, you’ll gain both a solid understanding of graph machine learning theory and the skills to apply it to real-world challenges. *Email sign-up and proof of purchase required -What you will learn Implement graph ML algorithms with examples in StellarGraph, PyTorch Geometric, and DGL Apply graph analysis to dynamic datasets using temporal graph ML Enhance NLP and text analytics with graph-based techniques Solve complex real-world problems with graph machine learning Build and scale graph-powered ML applications effectively Deploy and scale your application seamlessly Who this book is for This book is for data scientists, ML professionals, and graph specialists looking to deepen their knowledge of graph data analysis or expand their machine learning toolkit. Prior knowledge of Python and basic machine learning principles is recommended.
Graph Deep Learning
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Author : Muhan Zhang (Computer scientist)
language : en
Publisher:
Release Date : 2019
Graph Deep Learning written by Muhan Zhang (Computer scientist) and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Electronic dissertations categories.
The past few years have seen the growing prevalence of deep neural networks on various application domains including image processing, computer vision, speech recognition, machine translation, self-driving cars, game playing, social networks, bioinformatics, and healthcare etc. Due to the broad applications and strong performance, deep learning, a subfield of machine learning and artificial intelligence, is changing everyone's life.Graph learning has been another hot field among the machine learning and data mining communities, which learns knowledge from graph-structured data. Examples of graph learning range from social network analysis such as community detection and link prediction, to relational machine learning such as knowledge graph completion and recommender systems, to mutli-graph tasks such as graph classification and graph generation etc.An emerging new field, graph deep learning, aims at applying deep learning to graphs. To deal with graph-structured data, graph neural networks (GNNs) are invented in recent years which directly take graphs as input and output graph/node representations. Although GNNs have shown superior performance than traditional methods in tasks such as semi-supervised node classification, there still exist a wide range of other important graph learning problems where either GNNs' applicabilities have not been explored or GNNs only have less satisfying performance.In this dissertation, we dive deeper into the field of graph deep learning. By developing new algorithms, architectures and theories, we push graph neural networks' boundaries to a much wider range of graph learning problems. The problems we have explored include: 1) graph classification; 2) medical ontology embedding; 3) link prediction; 4) recommender systems; 5) graph generation; and 6) graph structure optimization.We first focus on two graph representation learning problems: graph classification and medical ontology embedding.For graph classification, we develop a novel deep GNN architecture which aggregates node features through a novel SortPooling layer that replaces the simple summing used in previous works. We demonstrate its state-of-the-art graph classification performance on benchmark datasets. For medical ontology embedding, we propose a novel hierarchical attention propagation model, which uses attention mechanism to learn embeddings of medical concepts from hierarchically-structured medical ontologies such as ICD-9 and CCS. We validate the learned embeddings on sequential procedure/diagnosis prediction tasks with real patient data.Then we investigate GNNs' potential for predicting relations, specifically link prediction and recommender systems. For link prediction, we first develop a theory unifying various traditional link prediction heuristics, and then design a framework to automatically learn suitable heuristics from a given network based on GNNs. Our model shows unprecedented strong link prediction performance, significantly outperforming all traditional methods. For recommender systems, we propose a novel graph-based matrix completion model, which uses a GNN to learn graph structure features from the bipartite graph formed by user and item interactions. Our model not only outperforms various matrix completion baselines, but also demonstrates excellent transfer learning ability -- a model trained on MovieLens can be directly used to predict Douban movie ratings with high performance.Finally, we explore GNNs' applicability to graph generation and graph structure optimization. We focus on a specific type of graphs which usually carry computations on them, namely directed acyclic graphs (DAGs). We develop a variational autoencoder (VAE) for DAGs and prove that it can injectively map computations into a latent space. This injectivity allows us to perform optimization in the continuous latent space instead of the original discrete structure space. We then apply our VAE to two types of DAGs, neural network architectures and Bayesian networks. Experiments show that our model not only generates novel and valid DAGs, but also finds high-quality neural architectures and Bayesian networks through performing Bayesian optimization in its latent space.