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Author: Masahito Hasegawa
Publisher: Springer Science & Business Media
ISBN: 1447108655
Category : Computers
Languages : en
Pages : 139
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Book Description
Models of Sharing Graphs presents a sound mathematical basis for reasoning about models of computation involving shared resources, including graph rewriting systems, denotational semantics and concurrency theory. An algebraic approach, based on the language of category theory, is taken throughout this work, which enables the author to describe several aspects of the notion of sharing in a systematic way. In particular, a novel account of recursive computation created from cyclic sharing is developed using this framework.
Author: Masahito Hasegawa
Publisher: Springer Science & Business Media
ISBN: 1447108655
Category : Computers
Languages : en
Pages : 139
Get Book Here
Book Description
Models of Sharing Graphs presents a sound mathematical basis for reasoning about models of computation involving shared resources, including graph rewriting systems, denotational semantics and concurrency theory. An algebraic approach, based on the language of category theory, is taken throughout this work, which enables the author to describe several aspects of the notion of sharing in a systematic way. In particular, a novel account of recursive computation created from cyclic sharing is developed using this framework.
Author: M. Hasegawa
Publisher:
ISBN:
Category :
Languages : en
Pages :
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Book Description
Author: Stefano Guerrini
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
Author: William L. William L. Hamilton
Publisher: Springer Nature
ISBN: 3031015886
Category : Computers
Languages : en
Pages : 141
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Book Description
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:
Publisher:
ISBN:
Category :
Languages : en
Pages : 4
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Book Description
Author: Dean Lusher
Publisher: Cambridge University Press
ISBN: 0521193567
Category : Business & Economics
Languages : en
Pages : 361
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Book Description
This book provides an account of the theoretical and methodological underpinnings of exponential random graph models (ERGMs).
Author: Mark Needham
Publisher: "O'Reilly Media, Inc."
ISBN: 1492047635
Category : Computers
Languages : en
Pages : 297
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Book Description
Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark
Author: M. Paul
Publisher: Elsevier
ISBN: 1483298132
Category : Science
Languages : en
Pages : 623
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Book Description
Considers the application of modern control engineering on digital computers with a view to improving productivity and product quality, easing supervision of industrial processes and reducing energy consumption and pollution. The topics covered may be divided into two main subject areas: (1) applications of digital control - in the chemical and oil industries, in water turbines, energy and power systems, robotics and manufacturing, cement, metallurgical processes, traffic control, heating and cooling; (2) systems theoretical aspects of digital control - adaptive systems, control aspects, multivariable systems, optimization and reliability, modelling and identification, real-time software and languages, distributed systems and data networks. Contains 84 papers.
Author: Juanzi Li
Publisher: Springer
ISBN: 9811073597
Category : Computers
Languages : en
Pages : 186
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Book Description
This book constitutes the refereed proceedings of the Second China Conference on Knowledge Graph and Semantic Computing, CCKS 2017, held in Chengdu, China, in August 2017. The 11 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 85 submissions. The papers cover wide research fields including the knowledge graph, the Semantic Web, linked data, NLP, knowledge representation, graph databases.
Author: James Powell
Publisher: Elsevier
ISBN: 178063434X
Category : Language Arts & Disciplines
Languages : en
Pages : 269
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Book Description
Graphs are about connections, and are an important part of our connected and data-driven world. A Librarian's Guide to Graphs, Data and the Semantic Web is geared toward library and information science professionals, including librarians, software developers and information systems architects who want to understand the fundamentals of graph theory, how it is used to represent and explore data, and how it relates to the semantic web. This title provides a firm grounding in the field at a level suitable for a broad audience, with an emphasis on open source solutions and what problems these tools solve at a conceptual level, with minimal emphasis on algorithms or mathematics. The text will also be of special interest to data science librarians and data professionals, since it introduces many graph theory concepts by exploring data-driven networks from various scientific disciplines. The first two chapters consider graphs in theory and the science of networks, before the following chapters cover networks in various disciplines. Remaining chapters move on to library networks, graph tools, graph analysis libraries, information problems and network solutions, and semantic graphs and the semantic web. Provides an accessible introduction to network science that is suitable for a broad audience Devotes several chapters to a survey of how graph theory has been used in a number of scientific data-driven disciplines Explores how graph theory could aid library and information scientists
