Products related to Graphs:
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Deep Learning on Graphs
Deep learning on graphs has become one of the hottest topics in machine learning.The book consists of four parts to best accommodate our readers with diverse backgrounds and purposes of reading.Part 1 introduces basic concepts of graphs and deep learning; Part 2 discusses the most established methods from the basic to advanced settings; Part 3 presents the most typical applications including natural language processing, computer vision, data mining, biochemistry and healthcare; and Part 4 describes advances of methods and applications that tend to be important and promising for future research.The book is self-contained, making it accessible to a broader range of readers including (1) senior undergraduate and graduate students; (2) practitioners and project managers who want to adopt graph neural networks into their products and platforms; and (3) researchers without a computer science background who want to use graph neural networks to advance their disciplines.
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Charts & Graphs
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Graphs & Digraphs
Graphs & Digraphs, Seventh Edition masterfully employs student-friendly exposition, clear proofs, abundant examples, and numerous exercises to provide an essential understanding of the concepts, theorems, history, and applications of graph theory.This classic text, widely popular among students and instructors alike for decades, is thoroughly streamlined in this new, seventh edition, to present a text consistent with contemporary expectations. Changes and updates to this edition include:A rewrite of four chapters from the ground upStreamlining by over a third for efficient, comprehensive coverage of graph theoryFlexible structure with foundational Chapters 1–6 and customizable topics in Chapters 7–11Incorporation of the latest developments in fundamental graph theoryStatements of recent groundbreaking discoveries, even if proofs are beyond scopeCompletely reorganized chapters on traversability, connectivity, coloring, and extremal graph theory to reflect recent developmentsThe text remains the consummate choice for an advanced undergraduate level or introductory graduate-level course exploring the subject’s fascinating history, while covering a host of interesting problems and diverse applications.Our major objective is to introduce and treat graph theory as the beautiful area of mathematics we have always found it to be.We have striven to produce a reader-friendly, carefully written book that emphasizes the mathematical theory of graphs, in all their forms.While a certain amount of mathematical maturity, including a solid understanding of proof, is required to appreciate the material, with a small number of exceptions this is the only pre-requisite. In addition, owing to the exhilarating pace of progress in the field, there have been countless developments in fundamental graph theory ever since the previous edition, and many of these discoveries have been incorporated into the book.Of course, some of the proofs of these results are beyond the scope of the book, in which cases we have only included their statements.In other cases, however, these new results have led us to completely reorganize our presentation.Two examples are the chapters on coloring and extremal graph theory.
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Teaching, Tutoring and Training in the Lifelong Learning Sector
This core text provides comprehensive support for pre-service and in-service trainee teachers in the Lifelong Learning Sector covering all they need to know to achieve QTLS status. Supporting trainees through all stages of their professional development, the text takes the reader through the theoretical background underpinning teaching and learning and offers practical guidance on day-to-day challenges. This fourth edition has been fully revised and updated and includes a new chapter on teaching practice with notes on observation and lesson planning.New information on behaviour management has been added to support trainees in an aspect of teaching that many find challenging.
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Are the graphs identical?
No, the graphs are not identical. While they may have similar shapes and patterns, there are differences in the specific data points and values represented on each graph. These differences could be due to variations in the data, different scales or axes used, or other factors that affect the visualization of the information. Therefore, it is important to carefully compare the details of each graph to understand the differences between them.
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How do you read graphs?
When reading graphs, it is important to first identify the axes and the variables being represented. Next, look at the scale of the axes to understand the range of values being shown. Pay attention to the trend or pattern in the data points, such as whether they are increasing, decreasing, or staying constant. Finally, analyze any labels, titles, or legends to fully interpret the information being presented in the graph.
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What are self-complementary graphs?
Self-complementary graphs are graphs that are isomorphic to their own complement. In other words, if you take a graph and replace each edge with a non-edge and each non-edge with an edge, you will get the same graph. Self-complementary graphs have a number of interesting properties and are often used in graph theory to study symmetrical structures and relationships between vertices and edges. Examples of self-complementary graphs include the Petersen graph and the Paley graph.
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How to interpret mathematical graphs?
Mathematical graphs can be interpreted by analyzing the shape, slope, and intersection points of the lines or curves. The x-axis represents one variable, while the y-axis represents another variable. The point where the graph intersects the axes can provide important information, such as the intercepts. Additionally, the overall trend of the graph can indicate relationships between the variables, such as positive or negative correlations.
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Spectra of Graphs
This book gives an elementary treatment of the basic material about graph spectra, both for ordinary, and Laplace and Seidel spectra.The text progresses systematically, by covering standard topics before presenting some new material on trees, strongly regular graphs, two-graphs, association schemes, p-ranks of configurations and similar topics.Exercises at the end of each chapter provide practice and vary from easy yet interesting applications of the treated theory, to little excursions into related topics.Tables, references at the end of the book, an author and subject index enrich the text. Spectra of Graphs is written for researchers, teachers and graduate students interested in graph spectra.The reader is assumed to be familiar with basic linear algebra and eigenvalues, although some more advanced topics in linear algebra, like the Perron-Frobenius theorem and eigenvalue interlacing are included.
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Dynamics on Graphs
This extensive revision of the 2007 book 'Random Graph Dynamics,' covering the current state of mathematical research in the field, is ideal for researchers and graduate students.It considers a small number of types of graphs, primarily the configuration model and inhomogeneous random graphs.However, it investigates a wide variety of dynamics.The author describes results for the convergence to equilibrium for random walks on random graphs as well as topics that have emerged as mature research areas since the publication of the first edition, such as epidemics, the contact process, voter models, and coalescing random walk.Chapter 8 discusses a new challenging and largely uncharted direction: systems in which the graph and the states of their vertices coevolve.
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Polytopes and Graphs
This book introduces convex polytopes and their graphs, alongside the results and methodologies required to study them.It guides the reader from the basics to current research, presenting many open problems to facilitate the transition.The book includes results not previously found in other books, such as: the edge connectivity and linkedness of graphs of polytopes; the characterisation of their cycle space; the Minkowski decomposition of polytopes from the perspective of geometric graphs; Lei Xue's recent lower bound theorem on the number of faces of polytopes with a small number of vertices; and Gil Kalai's rigidity proof of the lower bound theorem for simplicial polytopes.This accessible introduction covers prerequisites from linear algebra, graph theory, and polytope theory.Each chapter concludes with exercises of varying difficulty, designed to help the reader engage with new concepts.These features make the book ideal for students and researchers new to the field.
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The Graphs Gaff
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'How do you draw graphs?'
To draw a graph, start by determining the x and y axes and labeling them with the appropriate variables. Then, plot the points by locating the x and y coordinates on the graph and marking them with a point. Connect the points with a line or curve to represent the relationship between the variables. Finally, label the graph with a title, axis labels, and any other necessary information to make it clear and understandable.
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How can I draw graphs?
You can draw graphs using various software and tools such as Microsoft Excel, Google Sheets, or graphing calculators. These tools allow you to input data and create different types of graphs such as line graphs, bar graphs, pie charts, and scatter plots. Additionally, you can also use programming languages like Python and R to create more customized and complex graphs. These tools provide a user-friendly interface and various options to customize the appearance and layout of the graphs.
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How can one modify graphs?
One can modify graphs by changing the scale of the axes, adding or removing data points, adjusting the appearance of the lines or bars, and adding labels or annotations to provide more context. Additionally, one can change the type of graph used to better represent the data, such as switching from a bar graph to a line graph. Modifying graphs allows for better visualization and understanding of the data being presented.
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What are graphs in mathematics?
In mathematics, a graph is a collection of points, called vertices, and lines or curves, called edges, that connect pairs of vertices. Graphs are used to represent relationships between objects or entities. They are often used to model real-world situations, such as social networks, transportation networks, and communication networks. Graph theory is a branch of mathematics that studies the properties and applications of graphs.
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