Download or read book Scaling Graph Neural Network Training on Large Graphs for Effectiveness and Efficiency written by Jingshu Peng and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Graph Representation Learning written by William L. William L. Hamilton and published by Springer Nature. This book was released on 2022-06-01 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. This book provides a synthesis and overview of graph representation learning. 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.
Download or read book Optimizing Graph Neural Network Training on Large Graphs written by Nickolas Stathas and published by . This book was released on 2021 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs can be used to represent many important classes of structured real-world data. For this reason, there has been an increase of research interest in various machine learning approaches to solve tasks such as link prediction and node property prediction. Graph Neural Network models demonstrate good performance on such tasks. However, the depth of the models and the size of the graphs they can be trained on is constrained either by the low processing throughput of CPUs or by the limited memory capacity of GPUs. Techniques such as neighborhood sampling are often used to create smaller mini-batch training examples that fit in GPU memory. In this thesis, I provide a systematic performance analysis of GNN training codes written using PyTorch Geometric, the most popular machine learning framework for GNNs. Through this performance analysis, I uncover significant performance bottlenecks related to neighborhood sampling and GPU data transfers. To address these issues, I create FastPyG: a performance-engineered fork of PyTorch Geometric, which achieves a 3-6× speedup over comparable PyTorch Geometric codes without impacting model accuracy. The core contribution included in FastPyG is fast_sampler, an efficient and parallel neighborhood sampling implementation in C++.
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.
Download or read book Introduction to Graph Neural Networks written by Zhiyuan Liu and published by Morgan & Claypool Publishers. This book was released on 2020-03-20 with total page 129 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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. 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. 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.
Download or read book Deep Learning and the Game of Go written by Kevin Ferguson and published by Simon and Schuster. This book was released on 2019-01-06 with total page 611 pages. Available in PDF, EPUB and Kindle. Book excerpt: Summary Deep Learning and the Game of Go teaches you how to apply the power of deep learning to complex reasoning tasks by building a Go-playing AI. After exposing you to the foundations of machine and deep learning, you'll use Python to build a bot and then teach it the rules of the game. Foreword by Thore Graepel, DeepMind Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology The ancient strategy game of Go is an incredible case study for AI. In 2016, a deep learning-based system shocked the Go world by defeating a world champion. Shortly after that, the upgraded AlphaGo Zero crushed the original bot by using deep reinforcement learning to master the game. Now, you can learn those same deep learning techniques by building your own Go bot! About the Book Deep Learning and the Game of Go introduces deep learning by teaching you to build a Go-winning bot. As you progress, you'll apply increasingly complex training techniques and strategies using the Python deep learning library Keras. You'll enjoy watching your bot master the game of Go, and along the way, you'll discover how to apply your new deep learning skills to a wide range of other scenarios! What's inside Build and teach a self-improving game AI Enhance classical game AI systems with deep learning Implement neural networks for deep learning About the Reader All you need are basic Python skills and high school-level math. No deep learning experience required. About the Author Max Pumperla and Kevin Ferguson are experienced deep learning specialists skilled in distributed systems and data science. Together, Max and Kevin built the open source bot BetaGo. Table of Contents PART 1 - FOUNDATIONS Toward deep learning: a machine-learning introduction Go as a machine-learning problem Implementing your first Go bot PART 2 - MACHINE LEARNING AND GAME AI Playing games with tree search Getting started with neural networks Designing a neural network for Go data Learning from data: a deep-learning bot Deploying bots in the wild Learning by practice: reinforcement learning Reinforcement learning with policy gradients Reinforcement learning with value methods Reinforcement learning with actor-critic methods PART 3 - GREATER THAN THE SUM OF ITS PARTS AlphaGo: Bringing it all together AlphaGo Zero: Integrating tree search with reinforcement learning
Download or read book Large scale Graph Analysis System Algorithm and Optimization written by Yingxia Shao and published by Springer Nature. This book was released on 2020-07-01 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces readers to a workload-aware methodology for large-scale graph algorithm optimization in graph-computing systems, and proposes several optimization techniques that can enable these systems to handle advanced graph algorithms efficiently. More concretely, it proposes a workload-aware cost model to guide the development of high-performance algorithms. On the basis of the cost model, the book subsequently presents a system-level optimization resulting in a partition-aware graph-computing engine, PAGE. In addition, it presents three efficient and scalable advanced graph algorithms – the subgraph enumeration, cohesive subgraph detection, and graph extraction algorithms. This book offers a valuable reference guide for junior researchers, covering the latest advances in large-scale graph analysis; and for senior researchers, sharing state-of-the-art solutions based on advanced graph algorithms. In addition, all readers will find a workload-aware methodology for designing efficient large-scale graph algorithms.
Download or read book Towards Expressive and Scalable Deep Representation Learning for Graphs written by Zhitao Ying and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The ubiquity of graph structures in sciences and industry necessitates effective and scalable machine learning models capable of capturing the underlying inductive biases of the relational data. However, traditional representation learning algorithms on graph structure faces many limitations. Firstly, traditional methods including matrix factorization and distributed embeddings cannot scale to large real-world graphs with billions of nodes and edges due to their sizes of parameter space. Secondly, they lack expressiveness compared to recent advances of deep learning architectures. Lastly, they fail in inductive scenarios where they required to make prediction on nodes unseen during training. Finally, interpretation of what model learns from data is elusive to domain experts. In this thesis I present a series of work that pioneers the use of graph neural networks (GNNs) to tackle the challenges of representation learning on graphs in the aspects of explainability, scalability, and expressiveness. In the first part, I demonstrate my framework of GraphSAGE as a general but powerful overarching graph neural network framework. To tackle the challenge of model interpretability with the new GraphSAGE framework, I further introduce an extension model to obtain meaningful explanations from the trained graph neural network model. Under the framework of GraphSAGE, the second part presents a series of works that improves the expressive power of GNNs through the use of hierarchical structure, geometric embedding space, as well as multi-hop attention. These GNN-based architectures achieved unprecedented performance improvement over traditional methods on tasks in a variety of contexts, such as graph classification for molecules, hierarchical knowledge graphs and large-scale citation networks. In the third part, I further demonstrate a variety of applications of GNNs. Based on GraphSAGE, I developed PinSAGE, the first deployed GNN model that scales to billion-sized graphs. PinSAGE is deployed at Pinterest, to make recommendations for billions of users at Pinterest. In the area of grahics and simulations, we apply expressive architectures to accurately predict the physics of different materials and allow generalization to unseen dynamic systems. Finally, I discuss BiDyn, a dynamic GNN model for abuse detection before concluding the thesis.
Download or read book Principles of Big Graph In depth Insight written by and published by Elsevier. This book was released on 2023-01-24 with total page 460 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Big Graph: In-depth Insight, Volume 128 in the Advances in Computer series, highlights new advances in the field with this new volume presenting interesting chapters on a variety of topics, including CESDAM: Centered subgraph data matrix for large graph representation, Bivariate, cluster and suitability analysis of NoSQL Solutions for big graph applications, An empirical investigation on Big Graph using deep learning, Analyzing correlation between quality and accuracy of graph clustering, geneBF: Filtering protein-coded gene graph data using bloom filter, Processing large graphs with an alternative representation, MapReduce based convolutional graph neural networks: A comprehensive review. Fast exact triangle counting in large graphs using SIMD acceleration, A comprehensive investigation on attack graphs, Qubit representation of a binary tree and its operations in quantum computation, Modified ML-KNN: Role of similarity measures and nearest neighbor configuration in multi label text classification on big social network graph data, Big graph based online learning through social networks, Community detection in large-scale real-world networks, Power rank: An interactive web page ranking algorithm, GA based energy efficient modelling of a wireless sensor network, The major challenges of big graph and their solutions: A review, and An investigation on socio-cyber crime graph. Provides an update on the issues and challenges faced by current researchers Updates on future research agendas Includes advanced topics for intensive research for researchers
Download or read book Concepts and Techniques of Graph Neural Networks written by Kumar, Vinod and published by IGI Global. This book was released on 2023-05-22 with total page 267 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in graph neural networks have expanded their capacities and expressive power. Furthermore, practical applications have begun to emerge in a variety of fields including recommendation systems, fake news detection, traffic prediction, molecular structure in chemistry, antibacterial discovery physics simulations, and more. As a result, a boom of research at the juncture of graph theory and deep learning has revolutionized many areas of research. However, while graph neural networks have drawn a lot of attention, they still face many challenges when it comes to applying them to other domains, from a conceptual understanding of methodologies to scalability and interpretability in a real system. Concepts and Techniques of Graph Neural Networks provides a stepwise discussion, an exhaustive literature review, detailed analysis and discussion, rigorous experimentation results, and application-oriented approaches that are demonstrated with respect to applications of graph neural networks. The book also develops the understanding of concepts and techniques of graph neural networks and establishes the familiarity of different real applications in various domains for graph neural networks. Covering key topics such as graph data, social networks, deep learning, and graph clustering, this premier reference source is ideal for industry professionals, researchers, scholars, academicians, practitioners, instructors, and students.
Download or read book Systems Optimizations for Learning and Processing on Large Scale Graphs written by Morteza Ramezani and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graphs are powerful data representations favored in many computation domains and graph-based data structures have grown in popularity in recent years. However processing large graphs and learning useful information from them are both data- and compute-intensive. Unlike traditional data structures, where the data independency allows for more parallelism and optimizations, the dependency between nodes in the graph leads to more memory footprint, limited parallelism, privacy concerns and excessive random memory accesses. These wide range of problems make the design of optimized systems for performing graph-related tasks quite challenging. Among these tasks, graph processing systems and graph learning are gaining increasing attention in recent years. In graph processing systems the objective is to design and implement an efficient system for running graph analytic tasks, such as graph traversal or node ranking. On the other hand, as deep neural networks have been proven successful in a diverse range of applications involving images and sequences, several efforts have emerged trying to generalize deep neural networks to the graphs. However in either cases, the aforementioned special characteristics of the graphs lead to extensive memory requirement, inefficient memory accesses latency and communication burden which hinder the performance of the systems. This also limits the usage of ever popular distributed settings for processing such a data type, as new challenges arise while adapting distributed systems for graphs. In this dissertation, we address the systems limitations for processing and learning on graphs, by introducing novel techniques inspired by both systems and theoretical aspects. More specifically, we exploit the approximate capability of graph processing applications to design and develop an approximate graph processing system, motivated by both approximate computing methods and approximate graph algorithms. We introduce a novel framework which unlike existing systems does not require significant pre-processing time, while maintaining the accuracy for the output. In the second part of this dissertation, we focus on learning on graphs and more specifically graph neural networks. We investigate the main bottlenecks for training the graph neural networks in both centralized and distributed settings. While the underlying root of inefficiency is the same in both cases, in centralized systems such as CPU-GPU the limited memory capacity of GPU leads to significant decrease in training performance. We proposed a new sampling technique that takes advantage of this architecture and the unique structure of graphs to compensate for the memory limitation. Furthermore, in the distributed learning system, we first examine the effect of utilizing existing platforms for learning on the graph and show that using current techniques can lead to significant reduction in model performance. Later we propose a novel approach to improve the accuracy of learning on graphs in distributed systems, by leveraging a global correction technique.
Download or read book Graph Neural Networks Foundations Frontiers and Applications written by Lingfei Wu and published by Springer Nature. This book was released on 2022-01-03 with total page 701 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.
Download or read book Machine Learning on Graphs and Its Applications written by Xiaoyun Wang and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning in graphs are developed rapidly these years, massive of explorations in graph machine learning algorithms and theories. The applications varies among new drug discovery, anomaly detection, and document classification. With the widely use of neural networks, parallel training and testing, it becomes important to speedup these processes. One application of static graph topology analysis is in social networks, a single user may create multiple accounts to spread his / her opinions and to influence others, by actively comment on different news pages. It would be beneficial to both social networks and their communities, to demote such abnormal activities, and the first step is to detect those accounts. However, the detection is challenging, because these accounts may have very realistic names and reasonable activity patterns. In Chapter 2, we investigate three different approaches, and propose using graph embedding together with semi-supervised learning, to predict whether a pair of accounts are created by the same user. We carry out extensive experimental analyses to understand how changes in the input data and algorithmic parameters optimization affect the prediction performance.To understand deeply how the fake accounts affect the social networks classification, we study the robustness of graph convolutional networks (GCNs) with fake nodes attacks. Previous work have shown that GCNs are vulnerable to adversarial perturbation on adjacency or feature matrices of existing nodes; however, such attacks are usually unrealistic in real applications. We propose a new type of ``fake node attacks'' to attack GCNs by adding malicious fake nodes. This is much more realistic than previous attacks;To conduct fake node attacks, a greedy algorithm is proposed to generate edges of malicious nodes and their corresponding featuresaiming to minimize the classification accuracy on the target nodes. In addition, we introduce a discriminator to classify malicious nodes from real nodes, and propose a Greedy-GAN attack to simultaneously update the discriminator and the attacker, to make malicious nodes indistinguishable from the real ones. Inspired by the previous works on adversarial defense for deep neural networks, and especially adversarial training algorithm, we propose a method called GraphDefense to defend against the adversarial perturbations. In addition, for our defense method, we could still maintain semi-supervised learning settings, without a large label rate.We also show that adversarial training in features is equivalent to adversarial training for edges with a small perturbation. Our experiments show that the proposed defense methods successfully increase the robustness of Graph Convolutional Networks. Furthermore, we show that with careful design, our proposed algorithm can scale to large graphs, such as Reddit dataset. Finally, in industry, the training and inference speed is crucial. And for accelerating the neural networks, Sparse Deep Neural Network has its own advantages. We demonstrate our solution to this challenge with GraphBLAST, a GraphBLAS implementation on the GPU, and compare it to SuiteSparse, a GraphBLAS implementation on the CPU. The GraphBLAST implementation is 1.94X faster than SuiteSparse; the primary opportunity to increase performance on the GPU is a higher-performance sparse-matrix-times-sparse-matrix (SpGEMM) kernel.
Download or read book Deep Learning on Graphs written by Yao Ma and published by Cambridge University Press. This book was released on 2021-09-23 with total page 340 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning on graphs has become one of the hottest topics in machine learning. The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading. Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research. The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
Download or read book Spatial Data and Intelligence written by Xiaofeng Meng and published by Springer Nature. This book was released on 2023-05-10 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 4th International Conference on Spatial Data and Intelligence, SpatialDI 2023, held in Nanchang, China, in April 13–15, 2023. The 18 full papers included in this book were carefully reviewed and selected from 68 submissions. They were organized in topical sections as follows: traffic management; visualization analysis; spatial big data analysis; spatiotemporal data mining; spatiotemporal data storage; and metaverse.
Download or read book Graph Neural Networks in Action written by Keita Broadwater and published by Manning. This book was released on 2023-03-28 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to powerful graph-based deep learning models! Learn how to build cutting-edge graph neural networks for recommendation engines, molecular modeling, and more. Graph Neural Networks in Action teaches you to create powerful deep learning models for working with graph data. You’ll learn how to both design and train your models, and how to develop them into practical applications you can deploy to production. In Graph Neural Networks in Action you’ll create deep learning models that are perfect for working with interconnected graph data. Start with a comprehensive introduction to graph data’s unique properties. Then, dive straight into building real-world models, including GNNs that can generate node embeddings from a social network, recommend eCommerce products, and draw insights from social sites. This comprehensive guide contains coverage of the essential GNN libraries, including PyTorch Geometric, DeepGraph Library, and Alibaba’s GraphScope for training at scale. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.
