Are you looking for read ebook online? Search for your book and save it on your Kindle device, PC, phones or tablets. Download Just Graph It! PDF full book. Access full book title Just Graph It! by Sandi Hill. Download full books in PDF and EPUB format.
Author: Sandi Hill Publisher: Creative Teaching Press ISBN: 9781574713756 Category : Juvenile Fiction Languages : en Pages : 20
Book Description
Repetitive, predictable story lines and illustrations that match the text provide maximum support to the emergent reader. Engaging stories promote reading comprehension, and easy and fun activities on the inside back covers extend learning. Great for Reading First, Fluency, Vocabulary, Text Comprehension, and ESL/ELL!
Author: Sandi Hill Publisher: Creative Teaching Press ISBN: 9781574713756 Category : Juvenile Fiction Languages : en Pages : 20
Book Description
Repetitive, predictable story lines and illustrations that match the text provide maximum support to the emergent reader. Engaging stories promote reading comprehension, and easy and fun activities on the inside back covers extend learning. Great for Reading First, Fluency, Vocabulary, Text Comprehension, and ESL/ELL!
Author: Richard J. Trudeau Publisher: Courier Corporation ISBN: 0486318664 Category : Mathematics Languages : en Pages : 242
Book Description
Aimed at "the mathematically traumatized," this text offers nontechnical coverage of graph theory, with exercises. Discusses planar graphs, Euler's formula, Platonic graphs, coloring, the genus of a graph, Euler walks, Hamilton walks, more. 1976 edition.
Author: W. T. Tutte Publisher: Clarendon Press ISBN: 0191637785 Category : Mathematics Languages : en Pages : 164
Book Description
This book provides a unique and unusual introduction to graph theory by one of the founding fathers, and will be of interest to all researchers in the subject. It is not intended as a comprehensive treatise, but rather as an account of those parts of the theory that have been of special interest to the author. Professor Tutte details his experience in the area, and provides a fascinating insight into how he was led to his theorems and the proofs he used. As well as being of historical interest it provides a useful starting point for research, with references to further suggested books as well as the original papers. The book starts by detailing the first problems worked on by Professor Tutte and his colleagues during his days as an undergraduate member of the Trinity Mathematical Society in Cambridge. It covers subjects such as comnbinatorial problems in chess, the algebraicization of graph theory, reconstruction of graphs, and the chromatic eigenvalues. In each case fascinating historical and biographical information about the author's research is provided.
Author: William L. William L. Hamilton Publisher: Springer Nature ISBN: 3031015886 Category : Computers Languages : en Pages : 141
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: Gary Chartrand Publisher: Courier Corporation ISBN: 0486297306 Category : Mathematics Languages : en Pages : 466
Book Description
Written by two prominent figures in the field, this comprehensive text provides a remarkably student-friendly approach. Its sound yet accessible treatment emphasizes the history of graph theory and offers unique examples and lucid proofs. 2004 edition.
Author: Cleveland Publisher: CRC Press ISBN: 9780534091446 Category : Mathematics Languages : en Pages : 456
Book Description
The essential characteristic of a dynamic graphical method is the direct manipulation of elements of a graph on a computer screen, which in high-performance implementations, the elements change virtually instantaneously on the screen. This book contains a collection of papers about dynamic graphics dating from the late 1960s to 1988. Although technology has advanced considerably, the fundamental ideas about basic graphical principles and data-analytic goals are still relevant today.
Author: Ian Robinson Publisher: "O'Reilly Media, Inc." ISBN: 1449356222 Category : Computers Languages : en Pages : 161
Book Description
Discover how graph databases can help you manage and query highly connected data. With this practical book, you’ll learn how to design and implement a graph database that brings the power of graphs to bear on a broad range of problem domains. Whether you want to speed up your response to user queries or build a database that can adapt as your business evolves, this book shows you how to apply the schema-free graph model to real-world problems. Learn how different organizations are using graph databases to outperform their competitors. With this book’s data modeling, query, and code examples, you’ll quickly be able to implement your own solution. Model data with the Cypher query language and property graph model Learn best practices and common pitfalls when modeling with graphs Plan and implement a graph database solution in test-driven fashion Explore real-world examples to learn how and why organizations use a graph database Understand common patterns and components of graph database architecture Use analytical techniques and algorithms to mine graph database information
Author: Sandi Hill
Author: Sandi Hill
Author: Will C. Howell
Author: Richard J. Trudeau
Author: W. T. Tutte
Author: William L. William L. Hamilton
Author: Gary Chartrand
Author: Cleveland
Author: Alan Gibbons
Author: Robin Nelson
Author: Ian Robinson