Modern Graph Theory Algorithms With Python

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Modern Graph Theory Algorithms With Python

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Author : Colleen M. Farrelly
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-06-07

Modern Graph Theory Algorithms With Python written by Colleen M. Farrelly and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-06-07 with Computers categories.

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

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Author : K. Erciyes
language : en
Publisher: Springer Nature
Release Date : 2021-11-17

Algebraic Graph Algorithms written by K. Erciyes and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-17 with Computers categories.

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.

Python For Graph And Network Analysis

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Author : Mohammed Zuhair Al-Taie
language : en
Publisher: Springer
Release Date : 2017-03-20

Python For Graph And Network Analysis written by Mohammed Zuhair Al-Taie and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-20 with Computers categories.

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

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Author : William Kocay
language : en
Publisher: CRC Press
Release Date : 2017-09-20

Graphs Algorithms And Optimization written by William Kocay and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-09-20 with Mathematics categories.

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 Machine Learning

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Author : Claudio Stamile
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-06-25

Graph Machine Learning written by Claudio Stamile and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-25 with Computers categories.

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.

Algorithmic Graph Theory

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Author : Alan Gibbons
language : en
Publisher: Cambridge University Press
Release Date : 1985-06-27

Algorithmic Graph Theory written by Alan Gibbons and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1985-06-27 with Computers categories.

An introduction to pure and applied graph theory with an emphasis on algorithms and their complexity.

Data Analysis With Python

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Author : David Taieb
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-31

Data Analysis With Python written by David Taieb and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12-31 with Computers categories.

Learn a modern approach to data analysis using Python to harness the power of programming and AI across your data. Detailed case studies bring this modern approach to life across visual data, social media, graph algorithms, and time series analysis. Key FeaturesBridge your data analysis with the power of programming, complex algorithms, and AIUse Python and its extensive libraries to power your way to new levels of data insightWork with AI algorithms, TensorFlow, graph algorithms, NLP, and financial time seriesExplore this modern approach across with key industry case studies and hands-on projectsBook Description Data Analysis with Python offers a modern approach to data analysis so that you can work with the latest and most powerful Python tools, AI techniques, and open source libraries. Industry expert David Taieb shows you how to bridge data science with the power of programming and algorithms in Python. You'll be working with complex algorithms, and cutting-edge AI in your data analysis. Learn how to analyze data with hands-on examples using Python-based tools and Jupyter Notebook. You'll find the right balance of theory and practice, with extensive code files that you can integrate right into your own data projects. Explore the power of this approach to data analysis by then working with it across key industry case studies. Four fascinating and full projects connect you to the most critical data analysis challenges you’re likely to meet in today. The first of these is an image recognition application with TensorFlow – embracing the importance today of AI in your data analysis. The second industry project analyses social media trends, exploring big data issues and AI approaches to natural language processing. The third case study is a financial portfolio analysis application that engages you with time series analysis - pivotal to many data science applications today. The fourth industry use case dives you into graph algorithms and the power of programming in modern data science. You'll wrap up with a thoughtful look at the future of data science and how it will harness the power of algorithms and artificial intelligence. What you will learnA new toolset that has been carefully crafted to meet for your data analysis challengesFull and detailed case studies of the toolset across several of today’s key industry contextsBecome super productive with a new toolset across Python and Jupyter NotebookLook into the future of data science and which directions to develop your skills nextWho this book is for This book is for developers wanting to bridge the gap between them and data scientists. Introducing PixieDust from its creator, the book is a great desk companion for the accomplished Data Scientist. Some fluency in data interpretation and visualization is assumed. It will be helpful to have some knowledge of Python, using Python libraries, and some proficiency in web development.

Graph Theory

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Author : Beril Sirmacek
language : en
Publisher: BoD – Books on Demand
Release Date : 2018-01-31

Graph Theory written by Beril Sirmacek and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-01-31 with Mathematics categories.

This book is prepared as a combination of the manuscripts submitted by respected mathematicians and scientists around the world. As an editor, I truly enjoyed reading each manuscript. Not only will the methods and explanations help you to understand more about graph theory, but I also hope you will find it joyful to discover ways that you can apply graph theory in your scientific field. I believe the book can be read from the beginning to the end at once. However, the book can also be used as a reference guide in order to turn back to it when it is needed. I have to mention that this book assumes the reader to have a basic knowledge about graph theory. The very basics of the theory and terms are not explained at the beginner level. I hope this book will support many applied and research scientists from different scientific fields.

Graph Data Modeling In Python

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Author : Gary Hutson
language : en
Publisher: Packt Publishing Ltd
Release Date : 2023-06-30

Graph Data Modeling In Python written by Gary Hutson and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-30 with Computers categories.

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.

Graph Theory And Algorithms

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Author : Nobuhiko Saito
language : en
Publisher:
Release Date : 1981

Graph Theory And Algorithms written by Nobuhiko Saito and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1981 with categories.