Download or read book Graph Neural Networks for Multimodal Learning and Representation written by Mahmoud Khademi and published by . This book was released on 2019 with total page 97 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recently, several deep learning models are proposed that operate on graph-structured data. These models, which are known as graph neural networks, emphasize on methods for reasoning about non-Euclidean data. By combining end-to-end and handcrafted learning, graph neural networks can supply both relational reasoning and compositionality which are extremely important in many emerging tasks. This new paradigm is also consistent with the attributes of human intelligence: a human represents complicated systems as compositions of simple units and their interactions. Another important feature of graph neural networks is that they can often support complex attention mechanisms, and learn rich contextual representations by sending messages across different components of the input data. The main focus of this thesis is to solve some multimodal learning tasks by either introducing new graph neural network architectures or extending the existing graph neural network models and applying them to solve the tasks. I address three tasks: visual question answering (VQA), scene graph generation, and automatic image caption generation. I show that graph neural networks are effective tools to achieve better performance on these tasks. Despite all the hype and excitements about the future influence of graph neural networks, an open question about graph neural networks remains: how can we obtain the (structure of) the graphs that graph neural networks perform on? That is, how can we transform sensory input data such as images and text into graphs. A second main emphasis of this thesis is, therefore, to introduce new techniques and algorithms to address this issue. We introduce a generative graph neural network model based on reinforcement learning and recurrent neural networks (RNNs) to extract a structured representation from sensory data. The specific contributions are the following: We introduce a new neural network architecture, Multimodal Neural Graph Memory Networks (MN-GMN), for the VQA task. A key issue for VQA is how to reason about information from different image regions that is relevant for answering the question. Our novel approach uses graph structure with different region features as node attributes and applies a recently proposed powerful graph neural network model, Graph Network (GN), to reason about objects and their interactions in the scene context. The flexibility of GNs allows us to integrate bimodal sources of local information, text and visual, both within and across each modality. Experiments show MN-GMN outperforms the state-of-the-art on Visual7W and VQA v2.0 datasets and achieves comparable to the state-of-the-art results on CLEVR dataset. We propose a new algorithm, called Deep Generative Probabilistic Graph Neural Networks (DG-PGNN), to generate a scene graph for an image. The input to DG-PGNN is an image, together with a set of region-grounded captions (RGCs) and object bounding-box proposals for the image. To generate the scene graph, DG-PGNN constructs and updates a new model, called a Probabilistic Graph Network (PGN). A PGN can be thought of as a scene graph with uncertainty: it represents each node and each edge by a CNN feature vector and defines a probability mass function (PMF) for node-type (object category) of each node and edge-type (predicate class) of each edge. The DG-PGNN sequentially adds a new node to the current PGN by learning the optimal ordering in a Deep Q-learning framework, where states are partial PGNs, actions choose a new node, and rewards are defined based on the ground-truth. After adding a node, DG-PGNN uses message passing to update the feature vectors of the current PGN by leveraging contextual relationship information, object co-occurrences, and language priors from captions. The updated features are then used to fine-tune the PMFs. Our experiments show that the proposed algorithm significantly outperforms the state-of-the-art results on the Visual Genome dataset for the scene graph generation. We present a novel context-aware attention-based deep architecture for image caption generation. Our architecture employs a Bidirectional Grid LSTM, which takes visual features of an image as input and learns complex spatial patterns based on a two-dimensional context, by selecting or ignoring its input. The Grid LSTM can be seen as a graph neural network model with a grid structure. The Grid LSTM has not been applied to the image caption generation task before. Another novel aspect is that we leverage a set of local RGCs obtained by transfer learning. The RGCs often describe the properties of the objects and their relationships in an image. To generate a global caption for the image, we integrate the spatial features from the Grid LSTM with the local region-grounded texts, using a two-layer Bidirectional LSTM. The first layer models the global scene context such as object presence. The second layer utilizes a novel dynamic spatial attention mechanism, based on another Grid LSTM, to generate the global caption word-by-word while considering the caption context around a word in both directions. Unlike recent models that use a soft attention mechanism, our dynamic spatial attention mechanism considers the spatial context of the image regions. Experimental results on the MS-COCO dataset show that our architecture outperforms the state-of-the-art.
Download or read book From Unimodal to Multimodal Machine Learning written by Blaž Škrlj and published by Springer Nature. This book was released on with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt:
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 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 Graph Representation Learning written by William L. Hamilton and published by Morgan & Claypool Publishers. This book was released on 2020-09-16 with total page 161 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Heterogeneous Graph Representation Learning and Applications written by Chuan Shi and published by Springer Nature. This book was released on 2022-01-30 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: Representation learning in heterogeneous graphs (HG) is intended to provide a meaningful vector representation for each node so as to facilitate downstream applications such as link prediction, personalized recommendation, node classification, etc. This task, however, is challenging not only because of the need to incorporate heterogeneous structural (graph) information consisting of multiple types of node and edge, but also the need to consider heterogeneous attributes or types of content (e.g. text or image) associated with each node. Although considerable advances have been made in homogeneous (and heterogeneous) graph embedding, attributed graph embedding and graph neural networks, few are capable of simultaneously and effectively taking into account heterogeneous structural (graph) information as well as the heterogeneous content information of each node. In this book, we provide a comprehensive survey of current developments in HG representation learning. More importantly, we present the state-of-the-art in this field, including theoretical models and real applications that have been showcased at the top conferences and journals, such as TKDE, KDD, WWW, IJCAI and AAAI. The book has two major objectives: (1) to provide researchers with an understanding of the fundamental issues and a good point of departure for working in this rapidly expanding field, and (2) to present the latest research on applying heterogeneous graphs to model real systems and learning structural features of interaction systems. To the best of our knowledge, it is the first book to summarize the latest developments and present cutting-edge research on heterogeneous graph representation learning. To gain the most from it, readers should have a basic grasp of computer science, data mining and machine learning.
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 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 Deep Neural Architectures for Automatic Representation Learning from Multimedia Multimodal Data written by Verdran Vukotic and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this dissertation, the thesis that deep neural networks are suited for analysis of visual, textual and fused visual and textual content is discussed. This work evaluates the ability of deep neural networks to learn automatic multimodal representations in either unsupervised or supervised manners and brings the following main contributions:1) Recurrent neural networks for spoken language understanding (slot filling): different architectures are compared for this task with the aim of modeling both the input context and output label dependencies.2) Action prediction from single images: we propose an architecture that allow us to predict human actions from a single image. The architecture is evaluated on videos, by utilizing solely one frame as input.3) Bidirectional multimodal encoders: the main contribution of this thesis consists of neural architecture that translates from one modality to the other and conversely and offers and improved multimodal representation space where the initially disjoint representations can translated and fused. This enables for improved multimodal fusion of multiple modalities. The architecture was extensively studied an evaluated in international benchmarks within the task of video hyperlinking where it defined the state of the art today.4) Generative adversarial networks for multimodal fusion: continuing on the topic of multimodal fusion, we evaluate the possibility of using conditional generative adversarial networks to lean multimodal representations in addition to providing multimodal representations, generative adversarial networks permit to visualize the learned model directly in the image domain.
Download or read book Responsible Graph Neural Networks written by Mohamed Abdel-Basset and published by CRC Press. This book was released on 2023-06-05 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: More frequent and complex cyber threats require robust, automated, and rapid responses from cyber-security specialists. This book offers a complete study in the area of graph learning in cyber, emphasizing graph neural networks (GNNs) and their cyber-security applications. Three parts examine the basics, methods and practices, and advanced topics. The first part presents a grounding in graph data structures and graph embedding and gives a taxonomic view of GNNs and cyber-security applications. The second part explains three different categories of graph learning, including deterministic, generative, and reinforcement learning and how they can be used for developing cyber defense models. The discussion of each category covers the applicability of simple and complex graphs, scalability, representative algorithms, and technical details. Undergraduate students, graduate students, researchers, cyber analysts, and AI engineers looking to understand practical deep learning methods will find this book an invaluable resource.
Download or read book Graph Neural Networks for Natural Language Processing written by Yu Chen and published by . This book was released on 2023-01-25 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has become the dominant approach in addressing various tasks in Natural Language Processing (NLP). Although text inputs are typically represented as a sequence of tokens, there is a rich variety of NLP problems that can be best expressed with a graph structure. As a result, there is a surge of interest in developing new deep learning techniques on graphs for a large number of NLP tasks. In this monograph, the authors present a comprehensive overview on Graph Neural Networks (GNNs) for Natural Language Processing. They propose a new taxonomy of GNNs for NLP, which systematically organizes existing research of GNNs for NLP along three axes: graph construction, graph representation learning, and graph based encoder-decoder models. They further introduce a large number of NLP applications that exploits the power of GNNs and summarize the corresponding benchmark datasets, evaluation metrics, and open-source codes. Finally, they discuss various outstanding challenges for making the full use of GNNs for NLP as well as future research directions. This is the first comprehensive overview of Graph Neural Networks for Natural Language Processing. It provides students and researchers with a concise and accessible resource to quickly get up to speed with an important area of machine learning research.
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 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 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 Multimodal Brain Image Analysis and Mathematical Foundations of Computational Anatomy written by Dajiang Zhu and published by Springer Nature. This book was released on 2019-10-10 with total page 230 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed joint proceedings of the 4th International Workshop on Multimodal Brain Image Analysis, MBAI 2019, and the 7th International Workshop on Mathematical Foundations of Computational Anatomy, MFCA 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 16 full papers presented at MBAI 2019 and the 7 full papers presented at MFCA 2019 were carefully reviewed and selected. The MBAI papers intend to move forward the state of the art in multimodal brain image analysis, in terms of analysis methodologies, algorithms, software systems, validation approaches, benchmark datasets, neuroscience, and clinical applications. The MFCA papers are devoted to statistical and geometrical methods for modeling the variability of biological shapes. The goal is to foster the interactions between the mathematical community around shapes and the MICCAI community around computational anatomy applications.
Download or read book Mining on Graphs Graph Neural Network and Applications written by Yuxiang Ren and published by . This book was released on 2021 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: The graph is a data structure that exists widely around us, including traditional fields like physics, biology, and cosmology, as well as emergent fields like social networks, software engineering, and financial trading platforms. The graph-structured data contains objects (nodes) information and reflects their relationships (edges). The learning tasks become more challenging when considering the nodes and edge information simultaneously. Traditional machine learning methods focus on nodes' attributes but ignore the structural information. We are now in an era of deep learning, which outperforms traditional machine learning methods in a wide range of tasks and has a significant impact on our daily lives. Driving by deep learning and neural networks, the deep learning-based graph neural networks (GNNs) become convincing and attractive tools to handle this non-Euclidean data structure. The dissertation thesis includes my research works throughout the Ph. D. research in two directions of graph data mining. The first direction is about the innovation and improvement of graph neural networks. A large number of GNNs have appeared, but as a general representation learning model, there are still some difficult topics worth delving into. I focus on three questions: Unsupervised/self-supervised Learning of GNNs, GNNs for heterogeneous graphs, and Training larger and deeper GNNs. Concerning unsupervised/self-supervised learning of GNNs, the dissertation introduces my research works contributing to it in Chapter 3 and Chapter 4. In Chapter 5, I introduce a mutual information maximization-based GNN for heterogeneous graph representation learning. Chapter 6 discusses my contributions to training larger and deeper GNNs through a subgraph-based learning framework. The other direction is the Application of GNNs in Real-world Topics. As an effective tool for processing graph data, GNNs being applied to solve real-world graph mining problems can further verify the effectiveness. Meanwhile, the application of GNNs requires a combination of domain knowledge and specific data modeling, which is also a challenge that needs to be addressed. In Chapter 7, I apply GNNs to the emerging and non-trivial topic of fake news detection. When dealing with the fake news detection topic, I innovate the GNNs model to handle the challenges of the fake news detection problem, which is critical for GNNs to exert the best effect. Experiments with real-world fake news data show that the novel GNN can outperform text-based models and other graph-based models, especially when using less labeled news data. In the last chapter, I provide concluding thoughts about this dissertation thesis.
Download or read book Proceedings of the 13th International Conference on Computer Engineering and Networks written by Yonghong Zhang and published by Springer Nature. This book was released on 2024-01-03 with total page 585 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to examine innovation in the fields of computer engineering and networking. The text covers important developments in areas such as artificial intelligence, machine learning, information analysis, communication system, computer modeling, internet of things. This book presents papers from the 13th International Conference on Computer Engineering and Networks (CENet2023) held in Wuxi, China on November 3-5, 2023.
