Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Hypergraph Computation
Top Free Python Books 🌠 - 100% Free or Open Source!
  • Title Hypergraph Computation
  • Authors Qionghai Dai, Yue Gao
  • Publisher: Springer; 1st ed. 2023 edition (May 16, 2023); eBook (Creative Commons Licensed)
  • License(s): Creative Commons License (CC)
  • Paperback: 260 pages
  • eBook: PDF and ePub
  • Language: English
  • ISBN-10: 9819901871
  • ISBN-13: 978-9819901876
  • Share This:  

Book Description

This book summarizes a new computing paradigm, called Hypergraph computation, which is to formulate the high-order correlations underneath the data using hypergraph, and then conduct semantic computing on the hypergraph for different applications.

The first comprehensive and systematic overview for Hypergraph computation. Rich blend of basic knowledge, theoretical analysis, algorithm introduction, and key applications. Describes hypergraph computation applications in computer vision, medical applications, etc.

About the Authors
  • Qionghai Dai is a Professor and the Dean of School of Information at Tsinghua University.
Reviews, Ratings, and Recommendations: Related Book Categories: Read and Download Links: Similar Books:
  • Planar Maps, Random Walks and Circle Packing (Asaf Nachmias)

    This open access book focuses on the interplay between random walks on planar maps and Koebe’s circle packing theorem. Entirely self-contained. Many classical proofs have been simplified and streamlined. Contains numerous useful exercises.

  • Graph Theory (Reinhard Diestel)

    This book 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.

  • Digraphs: Theory, Algorithms and Applications (J. Bang-Jensen)

    This book is an essential, comprehensive reference of Digraphs covering the theoretical aspects of the subject, focus on applications which include quantum mechanics, bioinformatics, embedded computing, and the travelling salesman problem.

  • Graph Theory - Advanced Algorithms and Applications

    Not only will the methods and explanations help you to understand more about graph theory, but you will find it joyful to discover ways that you can apply graph theory in your applications or scientific research.

  • Graph Theory and Complex Networks (Maarten van Steen)

    This book aims to explain the basics of graph theory that are needed at an introductory level for students in computer or information sciences. It also aims to provide an introduction to the modern field of network science.

  • Probability on Trees and Networks (Russell Lyons, et al.)

    This book is concerned with certain aspects of discrete probability on infinite graphs that are currently in vigorous development. Of course, finite graphs are analyzed as well, but usually with the aim of understanding infinite graphs and networks.

  • Random Graphs and Complex Networks (Remco van der Hofstad)

    This rigorous introduction to network science presents Random Graphs as models for real-world networks. Such networks have distinctive empirical properties and a wealth of new models have emerged to capture them.

  • Graph Algorithms: Practical Examples in Apache Spark and Neo4j

    This book is a practical guide to getting started with graph algorithms for developers and data scientists who have experience using Apache Spark or Neo4j. You'll walk through hands-on examples that show you how to use graph algorithms in Apache Spark/Neo4j.

  • A Survey of Statistical Network Models (Anna Goldenberg, et al.)

    This book aims to provide the reader with an entry point to the voluminous literature on statistical network modeling. It guides the reader through the development of key stochastic network models, touches upon a number of examples and commonalities.

  • Lecture Notes on Graph Theory (Tero Harju)

    These are introductory lecture notes on graph theory. It offers undergraduates a remarkably student-friendly introduction to graph theory and takes an engaging approach that emphasizes graph theory's history.

  • Algorithmic Graph Theory (David Joyner, et al)

    This is an introductory book on algorithmic graph theory. Theory and algorithms are illustrated using the Sage open source mathematics software. It's especially suitable for computer scientists and mathematicians interested in computational complexity.

  • Explorations in Algebraic Graph Theory (Chris Godsil, et al.)

    This book aims to express properties of graphs in algebraic terms, then to deduce theorems about them. It tackles the applications of linear algebra and matrix theory to the study of graphs; algebraic constructions such as adjacency matrix, using the Sage.

  • An Introduction to Combinatorics and Graph Theory

    This book walks the reader through the classic parts of Combinatorics and graph theory, while also discussing some recent progress in the area: on the one hand, providing material that will help students learn the basic techniques.

  • Graph Databases: New Opportunities for Connected Data

    This book provides a practical foundation for those who want to apply Graph Database to real-world business solutions. You'll learn why graph database are useful, where they're applicable, and how to design and implement solutions that use them.

  • Advances in Graph Algorithms (Ton Kloks, et al.)

    This is a book about some currently popular topics such as exponential algorithms, fixed-parameter algorithms and algorithms using decomposition trees of graphs which is one of focuses of the book - occupied a whole chapter.

  • Graph Theory with Applications (J.A. Bondy, et al.)

    The primary aim of this book is to present a coherent introduction to graph theory, suitable as a textbook for advanced undergraduate and beginning graduate students in mathematics and computer science.

Book Categories
:
Other Categories
Resources and Links