Download or read book Graph Neural Networks for Improved Interpretability and Efficiency written by Patrick Pho and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Attributed graph is a powerful tool to model real-life systems which exist in many domains such as social science, biology, e-commerce, etc. The behaviors of those systems are mostly defined by or dependent on their corresponding network structures. Graph analysis has become an important line of research due to the rapid integration of such systems into every aspect of human life and the profound impact they have on human behaviors. Graph structured data contains a rich amount of information from the network connectivity and the supplementary input features of nodes. Machine learning algorithms or traditional network science tools have limitation in their capability to make use of both network topology and node features. Graph Neural Networks (GNNs) provide an efficient framework combining both sources of information to produce accurate prediction for a wide range of tasks including node classification, link prediction, etc. The exponential growth of graph datasets drives the development of complex GNN models causing concerns about processing time and interpretability of the result. Another issue arises from the cost and limitation of collecting a large amount of annotated data for training deep learning GNN models. Apart from sampling issue, the existence of anomaly entities in the data might degrade the quality of the fitted models. In this dissertation, we propose novel techniques and strategies to overcome the above challenges. First, we present a flexible regularization scheme applied to the Simple Graph Convolution (SGC). The proposed framework inherits fast and efficient properties of SGC while rendering a sparse set of fitted parameter vectors, facilitating the identification of important input features. Next, we examine efficient procedures for collecting training samples and develop indicative measures as well as quantitative guidelines to assist practitioners in choosing the optimal sampling strategy to obtain data. We then improve upon an existing GNN model for the anomaly detection task. Our proposed framework achieves better accuracy and reliability. Lastly, we experiment with adapting the flexible regularization mechanism to link prediction task.

Download or read book Residual Attention Augmentation Graph Neural Network for Improved Node Classification Residual Attention Augmentation Graph Neural Network for Improved Node Classification written by Muhammad Affan Abbas and published by Infinite Study. This book was released on 2024-01-01 with total page 5 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.

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 Scalability and Interpretability of Graph Neural Networks for Small Molecules written by Manjot Sangha and published by . This book was released on 2018 with total page 98 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this thesis I examine the use of graph neural networks for prediction tasks in chemistry with an emphasis on interpretable and scalable methods. I propose a novel kernel-inspired graph neural network architecture, called a subgraph matching neural network (SMNN), which is designed to have all feature representations and weights be human interpretable. I show that this network can achieve competitive performance with common graph neural network baselines. I also show that the network is capable of learning features that allow for transfer learning to larger molecules with significantly better performance than some baselines. This provides evidence the network is learning chemically useful representations. I then propose a framework for defining graph pooling operations to improve the scalability of graph neural networks with molecule size. I empirically examine some examples of these graph pooling layers and show that they can provide a significant speed-up without hurting accuracy, and even improving accuracy in some cases. Finally an instance of the SMNN network with a pooling layer is shown to achieve state-of-the-art accuracy on the Harvard Clean Energy Project dataset.

Download or read book Introduction to Graph Neural Networks written by Zhiyuan Zhiyuan Liu and published by Springer Nature. This book was released on 2022-05-31 with total page 109 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book A Wavelet Tour of Signal Processing written by Stephane Mallat and published by Elsevier. This book was released on 1999-09-14 with total page 663 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is intended to serve as an invaluable reference for anyone concerned with the application of wavelets to signal processing. It has evolved from material used to teach "wavelet signal processing" courses in electrical engineering departments at Massachusetts Institute of Technology and Tel Aviv University, as well as applied mathematics departments at the Courant Institute of New York University and ÉcolePolytechnique in Paris. - Provides a broad perspective on the principles and applications of transient signal processing with wavelets - Emphasizes intuitive understanding, while providing the mathematical foundations and description of fast algorithms - Numerous examples of real applications to noise removal, deconvolution, audio and image compression, singularity and edge detection, multifractal analysis, and time-varying frequency measurements - Algorithms and numerical examples are implemented in Wavelab, which is a Matlab toolbox freely available over the Internet - Content is accessible on several level of complexity, depending on the individual reader's needs New to the Second Edition - Optical flow calculation and video compression algorithms - Image models with bounded variation functions - Bayes and Minimax theories for signal estimation - 200 pages rewritten and most illustrations redrawn - More problems and topics for a graduate course in wavelet signal processing, in engineering and applied mathematics

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 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 Advanced Theoretical Neural Networks written by Jamie Flux and published by Independently Published. This book was released on 2024-09-20 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: A deep dive into the theory and mathematics behind neural networks, beyond typical AI applications. Area of focus: - Grasp complex statistical learning theories and their application in neural frameworks. - Explore universal approximation theorems to understand network capabilities. - Delve into the trade-offs between neural network depth and width. - Analyze the optimization landscapes to enhance training performance. - Study advanced gradient optimization methods for efficient training. - Investigate generalization theories applicable to deep learning models. - Examine regularization techniques with a strong theoretical foundation. - Apply the Information Bottleneck principle for better learning insights. - Understand the role of stochasticity and its impact on neural networks. - Master Bayesian techniques for uncertainty quantification and posterior inference. - Model neural networks using dynamical systems theory for stability analysis. - Learn representation learning and the geometry of feature spaces for transfer learning. - Explore theoretical insights into Convolutional Neural Networks (CNNs). - Analyze Recurrent Neural Networks (RNNs) for sequence data and temporal predictions. - Discover the theoretical underpinnings of attention mechanisms and transformers. - Study generative models like VAEs and GANs for creating new data. - Dive into energy-based models and Boltzmann machines for unsupervised learning. - Understand neural tangent kernel frameworks and infinite width networks. - Examine symmetries and invariances in neural network design. - Explore optimization methodologies beyond traditional gradient descent. - Enhance model robustness by learning about adversarial examples. - Address challenges in continual learning and overcome catastrophic forgetting. - Interpret sparse coding theories and design efficient, interpretable models. - Link neural networks with differential equations for theoretical advancements. - Analyze graph neural networks for relational learning on complex data structures. - Grasp the principles of meta-learning for quick adaptation and hypothesis search. - Delve into quantum neural networks for pushing the boundaries of computation. - Investigate neuromorphic computing models such as spiking neural networks. - Decode neural networks' decisions through explainability and interpretability methods. - Reflect on the ethical and philosophical implications of advanced AI technologies. - Discuss the theoretical limitations and unresolved challenges of neural networks. - Learn how topological data analysis informs neural network decision boundaries.

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 Advances in Graph Neural Networks written by Chuan Shi and published by Springer Nature. This book was released on 2022-11-16 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive introduction to the foundations and frontiers of graph neural networks. In addition, the book introduces the basic concepts and definitions in graph representation learning and discusses the development of advanced graph representation learning methods with a focus on graph neural networks. The book providers researchers and practitioners with an understanding of the fundamental issues as well as a launch point for discussing the latest trends in the science. The authors emphasize several frontier aspects of graph neural networks and utilize graph data to describe pairwise relations for real-world data from many different domains, including social science, chemistry, and biology. Several frontiers of graph neural networks are introduced, which enable readers to acquire the needed techniques of advances in graph neural networks via theoretical models and real-world applications.

Download or read book Interpretable Machine Learning written by Christoph Molnar and published by Lulu.com. This book was released on 2020 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.

Download or read book Computational Science ICCS 2024 written by Leonardo Franco and published by Springer Nature. This book was released on with total page 402 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Graph Prompting Unlocking the Power of Graph Neural Networks and Prompt Engineering for Advanced AI Applications written by Anand Vemula and published by Anand Vemula. This book was released on with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Graph Prompting" explores the intersection of Graph Neural Networks (GNNs) and prompt engineering, providing a comprehensive guide on leveraging these technologies for advanced AI applications. The book is structured into several key sections, each delving into different aspects of graph-based AI. #### Fundamentals of Graph Theory The book begins by laying the foundation with essential concepts in graph theory, such as nodes, edges, types of graphs, and graph representations. It explains fundamental metrics like degree, centrality, and clustering coefficients, and covers important algorithms for pathfinding and connectivity. #### Introduction to Prompting The next section introduces prompting in AI, particularly for large language models (LLMs). It covers the basics of prompt engineering, types of prompts (instruction-based, task-based), and design principles. Techniques like contextual prompting, chain-of-thought prompting, and few-shot/zero-shot prompting are discussed, providing practical examples and use cases. #### Graph Neural Networks (GNNs) A comprehensive overview of GNNs follows, detailing their architecture and applications. Key models like Graph Convolutional Networks (GCNs), GraphSAGE, and Graph Attention Networks (GATs) are explained with examples. The section also covers advanced GNN models, including transformer-based graph models and attention mechanisms. #### Graph Prompting for LLMs This section focuses on integrating GNNs with LLMs. It explores techniques for using graph embeddings in prompting, enhancing the capabilities of LLMs in various tasks such as recommendation systems, anomaly detection, and question answering. Practical applications and case studies demonstrate the effectiveness of these integrations. #### Ethics and Fairness in Graph Prompting Ethical considerations are crucial, and the book addresses biases in graph data and fairness in graph algorithms. It discusses the ethical implications of using graph data and provides strategies to ensure fairness and mitigate biases. #### Practical Applications and Case Studies The book highlights real-world applications of graph prompting in healthcare, social networks, and recommendation systems. Each case study showcases the practical benefits and challenges of implementing these technologies in different domains. #### Implementation Guides and Tools For practitioners, the book offers step-by-step implementation guides, using popular libraries like PyTorch Geometric and DGL. Example projects provide hands-on experience, helping readers apply the concepts discussed. #### Future Trends and Conclusion The book concludes with a look at future trends in graph prompting, including scalable GNNs, graph-based reinforcement learning, and ethical AI. It encourages continuous exploration and adaptation to leverage the full potential of graph-based AI technologies. Overall, "Graph Prompting" is a detailed and practical guide, offering valuable insights and tools for leveraging GNNs and prompt engineering to advance AI applications across various domains.

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 Revolutionizing the AI Digital Landscape written by Alex Khang and published by CRC Press. This book was released on 2024-06-07 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book investigates the growing influence of artificial intelligence in the marketing sphere, providing insights into how AI can be harnessed for developing more effective and efficient marketing strategies. In addition, the book will also offer a comprehensive overview of the various digital marketing tools available to entrepreneurs, discussing their features, benefits, and potential drawbacks. This will help entrepreneurs make well-informed decisions when selecting the tools most suited to their needs and objectives. It is designed to help entrepreneurs develop and implement successful strategies, leveraging the latest tools and technologies to achieve their business goals. As the digital landscape continues to evolve rapidly, this book aims to serve as a valuable resource for entrepreneurs looking to stay ahead of the curve and capitalize on new opportunities. The book's scope encompasses a wide range of topics, including customer experience, content marketing, AI strategy, and digital marketing tools.

Download or read book Leveraging Graph Neural Networks for Efficient Word Representations written by Ryan Himes and published by . This book was released on 2024 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sentence structure can consist of a complex structure of tokens and relationships between tokens which can be hard to represent with only a sequential representation. To capture more information about sentence structure we propose representing a sentence as a graph of tokens and relationships between tokens to learn dynamic word embeddings. The graph structure is used as input for a graph neural network (GNN) to learn syntactic and semantic information about the sentence by learning a next word prediction task to learn the embeddings. We also experiment with using the graph structure as input for different natural language classification tasks. Results show that using the GNN ARMAConv on natural language classification tasks can increase accuracy over a sequential representation. Also, using the newly trained dynamic word embeddings achieves a higher validation accuracy compared to all other word embeddings tested on the tweet disaster classification data set.