Heterogeneous Graph Representation Learning And Applications
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| Author | : Chuan Shi |
| Publisher | : Springer Nature |
| Total Pages | : 329 |
| Release | : 2022-01-30 |
| Genre | : Computers |
| ISBN | : 9811661669 |
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.
| Author | : William L. William L. Hamilton |
| Publisher | : Springer Nature |
| Total Pages | : 141 |
| Release | : 2022-06-01 |
| Genre | : Computers |
| ISBN | : 3031015886 |
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.
| Author | : Lingfei Wu |
| Publisher | : Springer Nature |
| Total Pages | : 701 |
| Release | : 2022-01-03 |
| Genre | : Computers |
| ISBN | : 9811660549 |
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.
| Author | : Chuan Shi |
| Publisher | : Springer Nature |
| Total Pages | : 207 |
| Release | : 2022-11-16 |
| Genre | : Mathematics |
| ISBN | : 3031161742 |
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.
| Author | : Yuxiang Ren |
| Publisher | : |
| Total Pages | : 172 |
| Release | : 2021 |
| Genre | : Computer science |
| ISBN | : |
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.
| Author | : William L. Hamilton |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 161 |
| Release | : 2020-09-16 |
| Genre | : Computers |
| ISBN | : 168173964X |
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.
| Author | : Yizhou Sun |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 162 |
| Release | : 2012 |
| Genre | : Computers |
| ISBN | : 1608458806 |
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.
| Author | : Yao Ma |
| Publisher | : Cambridge University Press |
| Total Pages | : 339 |
| Release | : 2021-09-23 |
| Genre | : Computers |
| ISBN | : 1108831745 |
A comprehensive text on foundations and techniques of graph neural networks with applications in NLP, data mining, vision and healthcare.
| Author | : Cheng Yang |
| Publisher | : Morgan & Claypool Publishers |
| Total Pages | : 244 |
| Release | : 2021-03-25 |
| Genre | : Computers |
| ISBN | : 1636390455 |
This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.
| Author | : Maxime Labonne |
| Publisher | : Packt Publishing Ltd |
| Total Pages | : 354 |
| Release | : 2023-04-14 |
| Genre | : Computers |
| ISBN | : 1804610704 |
Design robust graph neural networks with PyTorch Geometric by combining graph theory and neural networks with the latest developments and apps Purchase of the print or Kindle book includes a free PDF eBook Key Features Implement state-of-the-art graph neural network architectures in Python Create your own graph datasets from tabular data Build powerful traffic forecasting, recommender systems, and anomaly detection applications Book Description Graph neural networks are a highly effective tool for analyzing data that can be represented as a graph, such as social networks, chemical compounds, or transportation networks. The past few years have seen an explosion in the use of graph neural networks, with their application ranging from natural language processing and computer vision to recommendation systems and drug discovery. Hands-On Graph Neural Networks Using Python begins with the fundamentals of graph theory and shows you how to create graph datasets from tabular data. As you advance, you'll explore major graph neural network architectures and learn essential concepts such as graph convolution, self-attention, link prediction, and heterogeneous graphs. Finally, the book proposes applications to solve real-life problems, enabling you to build a professional portfolio. The code is readily available online and can be easily adapted to other datasets and apps. By the end of this book, you'll have learned to create graph datasets, implement graph neural networks using Python and PyTorch Geometric, and apply them to solve real-world problems, along with building and training graph neural network models for node and graph classification, link prediction, and much more. What you will learn Understand the fundamental concepts of graph neural networks Implement graph neural networks using Python and PyTorch Geometric Classify nodes, graphs, and edges using millions of samples Predict and generate realistic graph topologies Combine heterogeneous sources to improve performance Forecast future events using topological information Apply graph neural networks to solve real-world problems Who this book is for This book is for machine learning practitioners and data scientists interested in learning about graph neural networks and their applications, as well as students looking for a comprehensive reference on this rapidly growing field. Whether you're new to graph neural networks or looking to take your knowledge to the next level, this book has something for you. Basic knowledge of machine learning and Python programming will help you get the most out of this book.
