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The Practitioner's Guide to Graph Data : Applying Graph Thinking and Graph Technologies to Solve Complex Problems
Graph data closes the gap between the way humans and computers view the world.While computers rely on static rows and columns of data, people navigate and reason about life through relationships.This practical guide demonstrates how graph data brings these two approaches together.By working with concepts from graph theory, database schema, distributed systems, and data analysis, you'll arrive at a unique intersection known as graph thinking. Authors Denise Koessler Gosnell and Matthias Broecheler show data engineers, data scientists, and data analysts how to solve complex problems with graph databases.You'll explore templates for building with graph technology, along with examples that demonstrate how teams think about graph data within an application. Build an example application architecture with relational and graph technologiesUse graph technology to build a Customer 360 application, the most popular graph data pattern todayDive into hierarchical data and troubleshoot a new paradigm that comes from working with graph dataFind paths in graph data and learn why your trust in different paths motivates and informs your preferencesUse collaborative filtering to design a Netflix-inspired recommendation system
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Graph Algorithms for Data Science
Graphs are the natural way to understand connected data.This book explores the most important algorithms and techniques for graphs in data science, with practical examples and concrete advice on implementation and deployment. In Graph Algorithms for Data Science you will learn: Labeled-property graph modelingConstructing a graph from structured data such as CSV or SQLNLP techniques to construct a graph from unstructured dataCypher query language syntax to manipulate data and extract insightsSocial network analysis algorithms like PageRank and community detectionHow to translate graph structure to a ML model input with node embedding modelsUsing graph features in node classification and link prediction workflows Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications like machine learning, fraud detection, and business data analysis.It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs.You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more.You don't need any graph experience to start benefiting from this insightful guide.These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects. about the technology Graphs reveal the relationships in your data.Tracking these interlinking connections reveals new insights and influences and lets you analyze each data point as part of a larger whole.This interconnected data is perfect for machine learning, as well as analyzing social networks, communities, and even product recommendations. about the book Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data.You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights.The book explores common and useful graph algorithms like PageRank and community detection/clustering algorithms.Each new algorithm you learn is instantly put into action to complete a hands-on data project, including modeling a social network!Finally, you'll learn how to utilize graphs to upgrade your machine learning, including utilizing node embedding models and graph neural networks.
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Graph Vision : Digital Architecture’s Skeletons
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Exploring Graphs with Elixir : Connect Data with Native Graph Libraries and Graph Databases
Data is everywhere - it's just not very well connected, which makes it super hard to relate dataset to dataset.Using graphs as the underlying glue, you can readily join data together and create navigation paths across diverse sets of data.Add Elixir, with its awesome power of concurrency, and you'll soon be mastering data networks.Learn how different graph models can be accessed and used from within Elixir and how you can build a robust semantics overlay on top of graph data structures.We'll start from the basics and examine the main graph paradigms.Get ready to embrace the world of connected data!Graphs provide an intuitive and highly flexible means for organizing and querying huge amounts of loosely coupled data items.These data networks, or graphs in math speak, are typically stored and queried using graph databases.Elixir, with its noted support for fault tolerance and concurrency, stands out as a language eminently suited to processing sparsely connected and distributed datasets. Using Elixir and graph-aware packages in the Elixir ecosystem, you'll easily be able to fit your data to graphs and networks, and gain new information insights.Build a testbed app for comparing native graph data with external graph databases.Develop a set of applications under a single umbrella app to drill down into graph structures.Build graph models in Elixir, and query graph databases of various stripes - using Cypher and Gremlin with property graphs and SPARQL with RDF graphs.Transform data from one graph modeling regime to another.Understand why property graphs are especially good at graph traversal problems, while RDF graphs shine at integrating different semantic models and can scale up to web proportions. Harness the outstanding power of concurrent processing in Elixir to work with distributed graph datasets and manage data at scale. What You Need:To follow along with the book, you should have Elixir 1.10+ installed.The book will guide you through setting up an umbrella application for a graph testbed using a variety of graph databases for which Java SDK 8+ is generally required.Instructions for installing the graph databases are given in an appendix.
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Which graph is the graph of f?
The graph of f is the second graph, which is a parabola that opens upward. This can be determined because the function f(x) = ax^2 + bx + c is a quadratic function, and the second graph is a parabola, which is the graph of a quadratic function. Additionally, the fact that the parabola opens upward indicates that the coefficient a in the function f(x) = ax^2 + bx + c is positive. Therefore, the second graph is the graph of f.
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How do you graph a graph with fractions?
To graph a graph with fractions, you can start by plotting the x and y intercepts. If the fractions are in the form of y = mx + b, where m is the slope and b is the y-intercept, you can plot the y-intercept at the point (0, b). Then, use the slope to find another point on the line by moving up or down based on the numerator of the fraction and left or right based on the denominator. Connect the two points to create the line. If the fractions are in the form of y = ax^2 + bx + c, you can use the x-intercepts and vertex to plot the graph.
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How can one recognize the function graph from the derivative graph?
To recognize the function graph from the derivative graph, one can look for key features such as peaks and valleys in the derivative graph, which correspond to zeros or extrema in the function graph. Additionally, the derivative graph crossing the x-axis indicates where the function graph has a maximum or minimum point. The derivative graph also helps identify where the function graph is increasing or decreasing by observing the positive or negative values of the derivative. By analyzing these features, one can infer the behavior and shape of the original function graph.
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What is the English translation for 'graph stretching' and 'graph compressing'?
The English translation for 'graph stretching' is to increase the size or scale of a graph, making it appear larger or more spread out. On the other hand, 'graph compressing' refers to reducing the size or scale of a graph, making it appear smaller or more condensed. These terms are commonly used in mathematics and data visualization to describe the manipulation of the dimensions of a graph.
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Graph Theory
This standard textbook on modern graph theory combines the authority of a classic with the engaging freshness of style that is the hallmark of active mathematics.It covers the core material of the subject, with concise yet complete proofs, while offering glimpses of more advanced methods in each field via one or two deeper results. This is a major new edition. Among many other improvements, it offers additional tools for applying the regularity lemma, brings the tangle theory of graph minors up to the cutting edge of current research, and addresses new topics such as chi-boundedness in perfect graph theory. The book can be used as a reliable text for an introductory graduate course and is also suitable for self-study. From the reviews:“Deep, clear, wonderful. This is a serious book about the heart of graph theory.It has depth and integrity.” Persi Diaconis & Ron Graham, SIAM Review“The book has received a very enthusiastic reception, which it amply deserves.A masterly elucidation of modern graph theory.” Bulletin of the Institute of Combinatorics and its Applications“Succeeds dramatically ... a hell of a good book.” MAA Reviews“ ... like listening to someone explain mathematics.” Bulletin of the AMS
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Graph Theory
This standard textbook of modern graph theory, now in its fifth edition, combines the authority of a classic with the engaging freshness of style that is the hallmark of active mathematics.It covers the core material of the subject with concise yet reliably complete proofs, while offering glimpses of more advanced methods in each field by one or two deeper results, again with proofs given in full detail. The book can be used as a reliable text for an introductory course, as a graduate text, and for self-study. From the reviews: “This outstanding book cannot be substituted with any other book on the present textbook market.It has every chance of becoming the standard textbook for graph theory.” Acta Scientiarum Mathematiciarum “Deep, clear, wonderful.This is a serious book about the heart of graph theory.It has depth and integrity.” Persi Diaconis & Ron Graham, SIAM Review “The book hasreceived a very enthusiastic reception, which it amply deserves.A masterly elucidation of modern graph theory.” Bulletin of the Institute of Combinatorics and its Applications “Succeeds dramatically ... a hell of a good book.” MAA Reviews “A highlight of the book is what is by far the best account in print of the Seymour-Robertson theory of graph minors.” Mathematika “ ... like listening to someone explain mathematics.” Bulletin of the AMS
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Graph Sampling
Many technological, socio-economic, environmental, biomedical phenomena exhibit an underlying graph structure.Valued graph allows one to incorporate the connections or links among the population units in addition.The links may provide effectively access to the part of population that is the primary target, which is the case for many unconventional sampling methods, such as indirect, network, line-intercept or adaptive cluster sampling. Or, one may be interested in the structure of the connections, in terms of the corresponding graph properties or parameters, such as when various breadth- or depth-first non-exhaustive search algorithms are applied to obtain compressed views of large often dynamic graphs.Graph sampling provides a statistical approach to study real graphs from either of these perspectives.It is based on exploring the variation over all possible sample graphs (or subgraphs) which can be taken from the given population graph, by means of the relevant known sampling probabilities.The resulting design-based inference is valid whatever the unknown properties of the given real graphs. One-of-a-kind treatise of multidisciplinary topics relevant to statistics, mathematics and data science. Probabilistic treatment of breadth-first and depth-first non-exhaustive search algorithms in graphs. Presenting cutting-edge theory and methods based on latest research. Pathfinding for future research on sampling from real graphs. Graph Sampling can primarily be used as a resource for researchers working with sampling or graph problems, and as the basis of an advanced course for post-graduate students in statistics, mathematics and data science.
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Applied Graph Data Science : Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases
Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Use Cases delineates how graph data science significantly empowers the application of data science.The book discusses the emerging paradigm of graph data science in detail along with its practical research and real-world applications.Readers will be enriched with the knowledge of graph data science, graph analytics, algorithms, databases, platforms, and use cases across a variety of research and topics and applications.This book also presents how graphs are used as a programming language, especially demonstrating how Sleptsov Net Computing can contribute as an entirely graphical concurrent processing language for supercomputers.Graph data science is emerging as an expressive and illustrative data structure for optimally representing a variety of data types and their insightful relationships.These data structures include graph query languages, databases, algorithms, and platforms.From here, powerful analytics methods and machine learning/deep learning (ML/DL) algorithms are quickly evolving to analyze and make sense out of graph data.As a result, ground-breaking use cases across scientific research topics and industry verticals are being developed using graph data representation and manipulation.A wide range of complex business and scientific research requirements are efficiently represented and solved through graph data analysis, and Applied Graph Data Science: Graph Algorithms and Platforms, Knowledge Graphs, Neural Networks, and Applied Graph Data Science gives readers both the conceptual foundations and technical methods for applying these powerful techniques.
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How do I create an Excel graph from a data table?
To create an Excel graph from a data table, first, select the data you want to include in the graph. Then, go to the "Insert" tab on the Excel ribbon and choose the type of graph you want to create, such as a bar graph, line graph, or pie chart. Excel will automatically generate a graph based on the selected data. You can further customize the graph by adding titles, labels, and changing the formatting to suit your needs.
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What is a graph?
A graph is a mathematical structure that consists of a set of vertices (nodes) connected by edges (links). It is used to represent relationships between different entities. Graphs are widely used in various fields such as computer science, social networks, transportation systems, and biology to model and analyze complex systems. They can be directed (edges have a specific direction) or undirected (edges have no specific direction).
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How do you graph the graph of a rational function in mathematics?
To graph a rational function, first identify the vertical and horizontal asymptotes by finding the values that make the denominator equal to zero and the degree of the numerator and denominator. Then, find the x-intercepts by setting the numerator equal to zero. Next, determine the behavior of the function near the asymptotes and x-intercepts. Finally, plot the points and sketch the graph, making sure to show the behavior near the asymptotes and x-intercepts.
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Can you draw a graph on the GTR using only the graph?
No, I cannot draw a graph on the GTR using only the graph. The GTR (Graphing Calculator) is a tool that allows users to input equations and functions to generate graphs. It does not have the capability to interpret a graph and create an equation or function from it. In order to create a graph on the GTR, you would need to input the equation or function that corresponds to the graph you want to see.
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