Modern Graph Theory Algorithms With Python

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Modern Graph Theory Algorithms with Python

Author : Colleen M. Farrelly
Publisher : Packt Publishing Ltd
ISBN 13 : 1805120174
Total Pages : 290 pages
Book Rating : 4.8/5 (51 download)

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Book Synopsis Modern Graph Theory Algorithms with Python by : Colleen M. Farrelly

Download or read book Modern Graph Theory Algorithms with Python written by Colleen M. Farrelly and published by Packt Publishing Ltd. This book was released on 2024-06-07 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve challenging and computationally intensive analytics problems by leveraging network science and graph algorithms Key Features Learn how to wrangle different types of datasets and analytics problems into networks Leverage graph theoretic algorithms to analyze data efficiently Apply the skills you gain to solve a variety of problems through case studies in Python Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWe are living in the age of big data, and scalable solutions are a necessity. Network science leverages the power of graph theory and flexible data structures to analyze big data at scale. This book guides you through the basics of network science, showing you how to wrangle different types of data (such as spatial and time series data) into network structures. You’ll be introduced to core tools from network science to analyze real-world case studies in Python. As you progress, you’ll find out how to predict fake news spread, track pricing patterns in local markets, forecast stock market crashes, and stop an epidemic spread. Later, you’ll learn about advanced techniques in network science, such as creating and querying graph databases, classifying datasets with graph neural networks (GNNs), and mining educational pathways for insights into student success. Case studies in the book will provide you with end-to-end examples of implementing what you learn in each chapter. By the end of this book, you’ll be well-equipped to wrangle your own datasets into network science problems and scale solutions with Python.What you will learn Transform different data types, such as spatial data, into network formats Explore common network science tools in Python Discover how geometry impacts spreading processes on networks Implement machine learning algorithms on network data features Build and query graph databases Explore new frontiers in network science such as quantum algorithms Who this book is for If you’re a researcher or industry professional analyzing data and are curious about network science approaches to data, this book is for you. To get the most out of the book, basic knowledge of Python, including pandas and NumPy, as well as some experience working with datasets is required. This book is also ideal for anyone interested in network science and learning how graph algorithms are used to solve science and engineering problems. R programmers may also find this book helpful as many algorithms also have R implementations.

Algebraic Graph Algorithms

Author : K. Erciyes
Publisher : Springer Nature
ISBN 13 : 3030878864
Total Pages : 229 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Algebraic Graph Algorithms by : K. Erciyes

Download or read book Algebraic Graph Algorithms written by K. Erciyes and published by Springer Nature. This book was released on 2021-11-17 with total page 229 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook discusses the design and implementation of basic algebraic graph algorithms, and algebraic graph algorithms for complex networks, employing matroids whenever possible. The text describes the design of a simple parallel matrix algorithm kernel that can be used for parallel processing of algebraic graph algorithms. Example code is presented in pseudocode, together with case studies in Python and MPI. The text assumes readers have a background in graph theory and/or graph algorithms.

Modern Graph Theory

Author : Bela Bollobas
Publisher : Springer Science & Business Media
ISBN 13 : 1461206197
Total Pages : 408 pages
Book Rating : 4.4/5 (612 download)

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Book Synopsis Modern Graph Theory by : Bela Bollobas

Download or read book Modern Graph Theory written by Bela Bollobas and published by Springer Science & Business Media. This book was released on 2013-12-01 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt: An in-depth account of graph theory, written for serious students of mathematics and computer science. It reflects the current state of the subject and emphasises connections with other branches of pure mathematics. Recognising that graph theory is one of several courses competing for the attention of a student, the book contains extensive descriptive passages designed to convey the flavour of the subject and to arouse interest. In addition to a modern treatment of the classical areas of graph theory, the book presents a detailed account of newer topics, including Szemerédis Regularity Lemma and its use, Shelahs extension of the Hales-Jewett Theorem, the precise nature of the phase transition in a random graph process, the connection between electrical networks and random walks on graphs, and the Tutte polynomial and its cousins in knot theory. Moreover, the book contains over 600 well thought-out exercises: although some are straightforward, most are substantial, and some will stretch even the most able reader.

Python for Graph and Network Analysis

Author : Mohammed Zuhair Al-Taie
Publisher : Springer
ISBN 13 : 3319530046
Total Pages : 214 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Python for Graph and Network Analysis by : Mohammed Zuhair Al-Taie

Download or read book Python for Graph and Network Analysis written by Mohammed Zuhair Al-Taie and published by Springer. This book was released on 2017-03-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research monograph provides the means to learn the theory and practice of graph and network analysis using the Python programming language. The social network analysis techniques, included, will help readers to efficiently analyze social data from Twitter, Facebook, LiveJournal, GitHub and many others at three levels of depth: ego, group, and community. They will be able to analyse militant and revolutionary networks and candidate networks during elections. For instance, they will learn how the Ebola virus spread through communities. Practically, the book is suitable for courses on social network analysis in all disciplines that use social methodology. In the study of social networks, social network analysis makes an interesting interdisciplinary research area, where computer scientists and sociologists bring their competence to a level that will enable them to meet the challenges of this fast-developing field. Computer scientists have the knowledge to parse and process data while sociologists have the experience that is required for efficient data editing and interpretation. Social network analysis has successfully been applied in different fields such as health, cyber security, business, animal social networks, information retrieval, and communications.

Graphs, Algorithms, and Optimization

Author : William Kocay
Publisher : CRC Press
ISBN 13 : 135198912X
Total Pages : 504 pages
Book Rating : 4.3/5 (519 download)

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Book Synopsis Graphs, Algorithms, and Optimization by : William Kocay

Download or read book Graphs, Algorithms, and Optimization written by William Kocay and published by CRC Press. This book was released on 2017-09-20 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph theory offers a rich source of problems and techniques for programming and data structure development, as well as for understanding computing theory, including NP-Completeness and polynomial reduction. A comprehensive text, Graphs, Algorithms, and Optimization features clear exposition on modern algorithmic graph theory presented in a rigorous yet approachable way. The book covers major areas of graph theory including discrete optimization and its connection to graph algorithms. The authors explore surface topology from an intuitive point of view and include detailed discussions on linear programming that emphasize graph theory problems useful in mathematics and computer science. Many algorithms are provided along with the data structure needed to program the algorithms efficiently. The book also provides coverage on algorithm complexity and efficiency, NP-completeness, linear optimization, and linear programming and its relationship to graph algorithms. Written in an accessible and informal style, this work covers nearly all areas of graph theory. Graphs, Algorithms, and Optimization provides a modern discussion of graph theory applicable to mathematics, computer science, and crossover applications.

Graph Theory with Applications

Author : John Adrian Bondy
Publisher : London : Macmillan Press
ISBN 13 :
Total Pages : 290 pages
Book Rating : 4.:/5 (318 download)

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Book Synopsis Graph Theory with Applications by : John Adrian Bondy

Download or read book Graph Theory with Applications written by John Adrian Bondy and published by London : Macmillan Press. This book was released on 1976 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Graph Algorithms

Author : Mark Needham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492047635
Total Pages : 297 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Graph Algorithms by : Mark Needham

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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

Graph Algorithms

Author : Shimon Even
Publisher : Cambridge University Press
ISBN 13 : 1139504150
Total Pages : pages
Book Rating : 4.1/5 (395 download)

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Book Synopsis Graph Algorithms by : Shimon Even

Download or read book Graph Algorithms written by Shimon Even and published by Cambridge University Press. This book was released on 2011-09-19 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Shimon Even's Graph Algorithms, published in 1979, was a seminal introductory book on algorithms read by everyone engaged in the field. This thoroughly revised second edition, with a foreword by Richard M. Karp and notes by Andrew V. Goldberg, continues the exceptional presentation from the first edition and explains algorithms in a formal but simple language with a direct and intuitive presentation. The book begins by covering basic material, including graphs and shortest paths, trees, depth-first-search and breadth-first search. The main part of the book is devoted to network flows and applications of network flows, and it ends with chapters on planar graphs and testing graph planarity.

Graph Machine Learning

Author : Claudio Stamile
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206755
Total Pages : 338 pages
Book Rating : 4.8/5 (2 download)

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Book Synopsis Graph Machine Learning by : Claudio Stamile

Download or read book Graph Machine Learning written by Claudio Stamile and published by Packt Publishing Ltd. This book was released on 2021-06-25 with total page 338 pages. Available in PDF, EPUB and Kindle. Book excerpt: Build machine learning algorithms using graph data and efficiently exploit topological information within your models Key Features Implement machine learning techniques and algorithms in graph data Identify the relationship between nodes in order to make better business decisions Apply graph-based machine learning methods to solve real-life problems Book Description Graph Machine Learning will introduce you to a set of tools used for processing network data and leveraging the power of the relation between entities that can be used for predictive, modeling, and analytics tasks. The first chapters will introduce you to graph theory and graph machine learning, as well as the scope of their potential use. You'll then learn all you need to know about the main machine learning models for graph representation learning: their purpose, how they work, and how they can be implemented in a wide range of supervised and unsupervised learning applications. You'll build a complete machine learning pipeline, including data processing, model training, and prediction in order to exploit the full potential of graph data. After covering the basics, you'll be taken through real-world scenarios such as extracting data from social networks, text analytics, and natural language processing (NLP) using graphs and financial transaction systems on graphs. You'll also learn how to build and scale out data-driven applications for graph analytics to store, query, and process network information, and explore the latest trends on graphs. By the end of this machine learning book, you will have learned essential concepts of graph theory and all the algorithms and techniques used to build successful machine learning applications. What you will learn Write Python scripts to extract features from graphs Distinguish between the main graph representation learning techniques Learn how to extract data from social networks, financial transaction systems, for text analysis, and more Implement the main unsupervised and supervised graph embedding techniques Get to grips with shallow embedding methods, graph neural networks, graph regularization methods, and more Deploy and scale out your application seamlessly Who this book is for This book is for data scientists, data analysts, graph analysts, and graph professionals who want to leverage the information embedded in the connections and relations between data points to boost their analysis and model performance using machine learning. It will also be useful for machine learning developers or anyone who wants to build ML-driven graph databases. A beginner-level understanding of graph databases and graph data is required, alongside a solid understanding of ML basics. You'll also need intermediate-level Python programming knowledge to get started with this book.

Graph Data Modeling in Python

Author : Gary Hutson
Publisher : Packt Publishing Ltd
ISBN 13 : 1804619345
Total Pages : 236 pages
Book Rating : 4.8/5 (46 download)

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Book Synopsis Graph Data Modeling in Python by : Gary Hutson

Download or read book Graph Data Modeling in Python written by Gary Hutson and published by Packt Publishing Ltd. This book was released on 2023-06-30 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to transform, store, evolve, refactor, model, and create graph projections using the Python programming language Purchase of the print or Kindle book includes a free PDF eBook Key Features Transform relational data models into graph data model while learning key applications along the way Discover common challenges in graph modeling and analysis, and learn how to overcome them Practice real-world use cases of community detection, knowledge graph, and recommendation network Book Description Graphs have become increasingly integral to powering the products and services we use in our daily lives, driving social media, online shopping recommendations, and even fraud detection. With this book, you'll see how a good graph data model can help enhance efficiency and unlock hidden insights through complex network analysis. Graph Data Modeling in Python will guide you through designing, implementing, and harnessing a variety of graph data models using the popular open source Python libraries NetworkX and igraph. Following practical use cases and examples, you'll find out how to design optimal graph models capable of supporting a wide range of queries and features. Moreover, you'll seamlessly transition from traditional relational databases and tabular data to the dynamic world of graph data structures that allow powerful, path-based analyses. As well as learning how to manage a persistent graph database using Neo4j, you'll also get to grips with adapting your network model to evolving data requirements. By the end of this book, you'll be able to transform tabular data into powerful graph data models. In essence, you'll build your knowledge from beginner to advanced-level practitioner in no time. What you will learn Design graph data models and master schema design best practices Work with the NetworkX and igraph frameworks in Python Store, query, ingest, and refactor graph data Store your graphs in memory with Neo4j Build and work with projections and put them into practice Refactor schemas and learn tactics for managing an evolved graph data model Who this book is for If you are a data analyst or database developer interested in learning graph databases and how to curate and extract data from them, this is the book for you. It is also beneficial for data scientists and Python developers looking to get started with graph data modeling. Although knowledge of Python is assumed, no prior experience in graph data modeling theory and techniques is required.

Algorithmic Graph Theory

Author : Alan Gibbons
Publisher : Cambridge University Press
ISBN 13 : 9780521288811
Total Pages : 280 pages
Book Rating : 4.2/5 (888 download)

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Book Synopsis Algorithmic Graph Theory by : Alan Gibbons

Download or read book Algorithmic Graph Theory written by Alan Gibbons and published by Cambridge University Press. This book was released on 1985-06-27 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to pure and applied graph theory with an emphasis on algorithms and their complexity.

Algorithms on Trees and Graphs

Author : Gabriel Valiente
Publisher : Springer Science & Business Media
ISBN 13 : 9783540435501
Total Pages : 508 pages
Book Rating : 4.4/5 (355 download)

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Book Synopsis Algorithms on Trees and Graphs by : Gabriel Valiente

Download or read book Algorithms on Trees and Graphs written by Gabriel Valiente and published by Springer Science & Business Media. This book was released on 2002-09-05 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.

Graphs, Networks and Algorithms

Author : Dieter Jungnickel
Publisher : Springer Science & Business Media
ISBN 13 : 3540269088
Total Pages : 616 pages
Book Rating : 4.5/5 (42 download)

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Book Synopsis Graphs, Networks and Algorithms by : Dieter Jungnickel

Download or read book Graphs, Networks and Algorithms written by Dieter Jungnickel and published by Springer Science & Business Media. This book was released on 2005-08-29 with total page 616 pages. Available in PDF, EPUB and Kindle. Book excerpt: Revised throughout Includes new chapters on the network simplex algorithm and a section on the five color theorem Recent developments are discussed

Algorithms on Trees and Graphs

Author : Gabriel Valiente
Publisher : Springer Nature
ISBN 13 : 3030818853
Total Pages : 392 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Algorithms on Trees and Graphs by : Gabriel Valiente

Download or read book Algorithms on Trees and Graphs written by Gabriel Valiente and published by Springer Nature. This book was released on 2021-10-11 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph algorithms is a well-established subject in mathematics and computer science. Beyond classical application fields, such as approximation, combinatorial optimization, graphics, and operations research, graph algorithms have recently attracted increased attention from computational molecular biology and computational chemistry. Centered around the fundamental issue of graph isomorphism, this text goes beyond classical graph problems of shortest paths, spanning trees, flows in networks, and matchings in bipartite graphs. Advanced algorithmic results and techniques of practical relevance are presented in a coherent and consolidated way. This book introduces graph algorithms on an intuitive basis followed by a detailed exposition in a literate programming style, with correctness proofs as well as worst-case analyses. Furthermore, full C++ implementations of all algorithms presented are given using the LEDA library of efficient data structures and algorithms.

Graph-based Keyword Spotting

Author : Stauffer Michael
Publisher : World Scientific
ISBN 13 : 9811206643
Total Pages : 296 pages
Book Rating : 4.8/5 (112 download)

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Book Synopsis Graph-based Keyword Spotting by : Stauffer Michael

Download or read book Graph-based Keyword Spotting written by Stauffer Michael and published by World Scientific. This book was released on 2019-07-24 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keyword Spotting (KWS) has been proposed as a flexible and more error-tolerant alternative to full transcriptions. In most cases, it allows to retrieve arbitrary query words in handwritten historical document.This comprehensive compendium gives a self-contained preamble and visually attractive description to the field of graph-based KWS. The volume highlights a profound insight into each step of the whole KWS pipeline, viz. image preprocessing, graph representation and graph matching.Written by two world-renowned co-authors, this unique title combines two very current research fields of graph-based pattern recognition and document analysis. The book serves as an attractive teaching material for graduate students, as well as a useful reference text for professionals, academics and researchers.

Graph Algorithms and Applications 3

Author : Giuseppe Liotta
Publisher : World Scientific
ISBN 13 : 9789812796608
Total Pages : 418 pages
Book Rating : 4.7/5 (966 download)

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Book Synopsis Graph Algorithms and Applications 3 by : Giuseppe Liotta

Download or read book Graph Algorithms and Applications 3 written by Giuseppe Liotta and published by World Scientific. This book was released on 2004-01-01 with total page 418 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book contains Volume 6 of the Journal of Graph Algorithms and Applications (JGAA) . JGAA is a peer-reviewed scientific journal devoted to the publication of high-quality research papers on the analysis, design, implementation, and applications of graph algorithms. Areas of interest include computational biology, computational geometry, computer graphics, computer-aided design, computer and interconnection networks, constraint systems, databases, graph drawing, graph embedding and layout, knowledge representation, multimedia, software engineering, telecommunications networks, user interfaces and visualization, and VLSI circuit design. Graph Algorithms and Applications 3 presents contributions from prominent authors and includes selected papers from the Symposium on Graph Drawing (1999 and 2000). All papers in the book have extensive diagrams and offer a unique treatment of graph algorithms focusing on the important applications. Contents: Triangle-Free Planar Graphs and Segment Intersection Graphs (N de Castro et al.); Traversing Directed Eulerian Mazes (S Bhatt et al.); A Fast Multi-Scale Method for Drawing Large Graphs (D Harel & Y Koren); GRIP: Graph Drawing with Intelligent Placement (P Gajer & S G Kobourov); Graph Drawing in Motion (C Friedrich & P Eades); A 6-Regular Torus Graph Family with Applications to Cellular and Interconnection Networks (M Iridon & D W Matula); and other papers. Readership: Researchers and practitioners in theoretical computer science, computer engineering, and combinatorics and graph theory.

Algorithms and Data Structures with Python

Author : Cuantum Technologies LLC
Publisher : Packt Publishing Ltd
ISBN 13 : 1836208545
Total Pages : 488 pages
Book Rating : 4.8/5 (362 download)

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Book Synopsis Algorithms and Data Structures with Python by : Cuantum Technologies LLC

Download or read book Algorithms and Data Structures with Python written by Cuantum Technologies LLC and published by Packt Publishing Ltd. This book was released on 2024-06-12 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Python and elevate your algorithmic skills with this comprehensive course. From introductory concepts to advanced computational problems, learn how to efficiently solve complex challenges and optimize your code. Key Features Comprehensive introduction to Python programming and algorithms Detailed exploration of data structures and sorting/searching techniques Advanced topics including graph algorithms and computational problem-solving Book DescriptionBegin your journey with an introduction to Python and algorithms, laying the groundwork for more complex topics. You will start with the basics of Python programming, ensuring a solid foundation before diving into more advanced and sophisticated concepts. As you progress, you'll explore elementary data containers, gaining an understanding of their role in algorithm development. Midway through the course, you’ll delve into the art of sorting and searching, mastering techniques that are crucial for efficient data handling. You will then venture into hierarchical data structures, such as trees and graphs, which are essential for understanding complex data relationships. By mastering algorithmic techniques, you’ll learn how to implement solutions for a variety of computational challenges. The latter part of the course focuses on advanced topics, including network algorithms, string and pattern deciphering, and advanced computational problems. You'll apply your knowledge through practical case studies and optimizations, bridging the gap between theoretical concepts and real-world applications. This comprehensive approach ensures you are well-prepared to handle any programming challenge with confidence.What you will learn Master sorting and searching algorithms Implement hierarchical data structures like trees and graphs Apply advanced algorithmic techniques to solve complex problems Optimize code for efficiency and performance Understand and implement advanced graph algorithms Translate theoretical concepts into practical, real-world solutions Who this book is for This course is designed for a diverse group of learners, including technical professionals, software developers, computer science students, and data enthusiasts. It caters to individuals who have a basic understanding of programming and are eager to deepen their knowledge of Python and algorithms. Whether you're a recent graduate, or an experienced developer looking to expand your skill set, this course is tailored to meet the needs of all types of audiences. Ideal for those aiming to strengthen their algorithmic thinking and improve their coding efficiency.