Network Science With Python And Networkx Quick Start Guide

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Network Science with Python and NetworkX Quick Start Guide

Author : Edward L. Platt
Publisher : Packt Publishing Ltd
Page : 181 pages
File Size : 45,7 Mb
Release : 2019-04-26
Category : Computers
ISBN : 9781789950410

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Network Science with Python and NetworkX Quick Start Guide by Edward L. Platt Pdf

Manipulate and analyze network data with the power of Python and NetworkX Key FeaturesUnderstand the terminology and basic concepts of network scienceLeverage the power of Python and NetworkX to represent data as a networkApply common techniques for working with network data of varying sizesBook Description NetworkX is a leading free and open source package used for network science with the Python programming language. NetworkX can track properties of individuals and relationships, find communities, analyze resilience, detect key network locations, and perform a wide range of important tasks. With the recent release of version 2, NetworkX has been updated to be more powerful and easy to use. If you’re a data scientist, engineer, or computational social scientist, this book will guide you in using the Python programming language to gain insights into real-world networks. Starting with the fundamentals, you’ll be introduced to the core concepts of network science, along with examples that use real-world data and Python code. This book will introduce you to theoretical concepts such as scale-free and small-world networks, centrality measures, and agent-based modeling. You’ll also be able to look for scale-free networks in real data and visualize a network using circular, directed, and shell layouts. By the end of this book, you’ll be able to choose appropriate network representations, use NetworkX to build and characterize networks, and uncover insights while working with real-world systems. What you will learnUse Python and NetworkX to analyze the properties of individuals and relationshipsEncode data in network nodes and edges using NetworkXManipulate, store, and summarize data in network nodes and edgesVisualize a network using circular, directed and shell layoutsFind out how simulating behavior on networks can give insights into real-world problemsUnderstand the ongoing impact of network science on society, and its ethical considerationsWho this book is for If you are a programmer or data scientist who wants to manipulate and analyze network data in Python, this book is perfect for you. Although prior knowledge of network science is not necessary, some Python programming experience will help you understand the concepts covered in the book easily.

Network Science with Python

Author : David Knickerbocker
Publisher : Packt Publishing Ltd
Page : 414 pages
File Size : 48,9 Mb
Release : 2023-02-28
Category : Computers
ISBN : 9781801075213

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Network Science with Python by David Knickerbocker Pdf

Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in color Key FeaturesCreate networks using data points and informationLearn to visualize and analyze networks to better understand communitiesExplore the use of network data in both - supervised and unsupervised machine learning projectsPurchase of the print or Kindle book includes a free PDF eBookBook Description Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard – practical data sets. You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference. As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level. By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you. What you will learnExplore NLP, network science, and social network analysisApply the tech stack used for NLP, network science, and analysisExtract insights from NLP and network dataGenerate personalized NLP and network projectsAuthenticate and scrape tweets, connections, the web, and data streamsDiscover the use of network data in machine learning projectsWho this book is for Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both – social science and programming backgrounds will find a new perspective and add a feather to their hat.

A First Course in Network Science

Author : Filippo Menczer,Santo Fortunato,Clayton A. Davis
Publisher : Cambridge University Press
Page : 275 pages
File Size : 40,9 Mb
Release : 2020-01-30
Category : Business & Economics
ISBN : 9781108471138

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A First Course in Network Science by Filippo Menczer,Santo Fortunato,Clayton A. Davis Pdf

A practical introduction to network science for students across business, cognitive science, neuroscience, sociology, biology, engineering and other disciplines.

Complex Network Analysis in Python

Author : Dmitry Zinoviev
Publisher : Pragmatic Bookshelf
Page : 343 pages
File Size : 54,9 Mb
Release : 2018-01-19
Category : Computers
ISBN : 9781680505405

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Complex Network Analysis in Python by Dmitry Zinoviev Pdf

Construct, analyze, and visualize networks with networkx, a Python language module. Network analysis is a powerful tool you can apply to a multitude of datasets and situations. Discover how to work with all kinds of networks, including social, product, temporal, spatial, and semantic networks. Convert almost any real-world data into a complex network--such as recommendations on co-using cosmetic products, muddy hedge fund connections, and online friendships. Analyze and visualize the network, and make business decisions based on your analysis. If you're a curious Python programmer, a data scientist, or a CNA specialist interested in mechanizing mundane tasks, you'll increase your productivity exponentially. Complex network analysis used to be done by hand or with non-programmable network analysis tools, but not anymore! You can now automate and program these tasks in Python. Complex networks are collections of connected items, words, concepts, or people. By exploring their structure and individual elements, we can learn about their meaning, evolution, and resilience. Starting with simple networks, convert real-life and synthetic network graphs into networkx data structures. Look at more sophisticated networks and learn more powerful machinery to handle centrality calculation, blockmodeling, and clique and community detection. Get familiar with presentation-quality network visualization tools, both programmable and interactive--such as Gephi, a CNA explorer. Adapt the patterns from the case studies to your problems. Explore big networks with NetworKit, a high-performance networkx substitute. Each part in the book gives you an overview of a class of networks, includes a practical study of networkx functions and techniques, and concludes with case studies from various fields, including social networking, anthropology, marketing, and sports analytics. Combine your CNA and Python programming skills to become a better network analyst, a more accomplished data scientist, and a more versatile programmer. What You Need: You will need a Python 3.x installation with the following additional modules: Pandas (>=0.18), NumPy (>=1.10), matplotlib (>=1.5), networkx (>=1.11), python-louvain (>=0.5), NetworKit (>=3.6), and generalizesimilarity. We recommend using the Anaconda distribution that comes with all these modules, except for python-louvain, NetworKit, and generalizedsimilarity, and works on all major modern operating systems.

Practical Data Science with Python

Author : Nathan George
Publisher : Packt Publishing Ltd
Page : 621 pages
File Size : 42,5 Mb
Release : 2021-09-30
Category : Computers
ISBN : 9781801076654

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Practical Data Science with Python by Nathan George Pdf

Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.

Artificial Intelligence of Things

Author : Rama Krishna Challa,Gagangeet Singh Aujla,Lini Mathew,Amod Kumar,Mala Kalra,S. L. Shimi,Garima Saini,Kanika Sharma
Publisher : Springer Nature
Page : 409 pages
File Size : 46,8 Mb
Release : 2023-12-02
Category : Computers
ISBN : 9783031487811

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Artificial Intelligence of Things by Rama Krishna Challa,Gagangeet Singh Aujla,Lini Mathew,Amod Kumar,Mala Kalra,S. L. Shimi,Garima Saini,Kanika Sharma Pdf

These two volumes constitute the revised selected papers of First International Conference, ICAIoT 2023, held in Chandigarh, India, during March 30–31, 2023. The 47 full papers and the 10 short papers included in this volume were carefully reviewed and selected from 401 submissions. The two books focus on research issues, opportunities and challenges of AI and IoT applications. They present the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of AI algorithms implementation in IoT Systems

Designing and Developing Innovative Mobile Applications

Author : Samanta, Debabrata
Publisher : IGI Global
Page : 456 pages
File Size : 45,6 Mb
Release : 2023-04-12
Category : Technology & Engineering
ISBN : 9781668485842

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Designing and Developing Innovative Mobile Applications by Samanta, Debabrata Pdf

Since mobile communication has become so ingrained in our daily lives, many people find it difficult to function without a cellphone. When the phone first came out, the only commonly used features were calling and sending text messages (texts). The intelligent mobile phone has proven to be a multipurpose tool that works best for communication and aids in learning, earning, and having fun. This in turn prompted several developers to consider creating mobile applications. Designing and Developing Innovative Mobile Applications focuses on the fundamentals of the Android OS and its device features, the deployment of any Android application, and the activities and intents of Android programming. Covering key topics such as mobile pages, software development, and communication, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.

Network Science

Author : Albert-László Barabási,Márton PÃ3sfai
Publisher : Cambridge University Press
Page : 477 pages
File Size : 41,9 Mb
Release : 2016-07-21
Category : Computers
ISBN : 9781107076266

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Network Science by Albert-László Barabási,Márton PÃ3sfai Pdf

Illustrated throughout in full colour, this pioneering text is the only book you need for an introduction to network science.

Parallel Computing Technologies

Author : Victor Malyshkin
Publisher : Springer Nature
Page : 212 pages
File Size : 54,9 Mb
Release : 2023-08-14
Category : Computers
ISBN : 9783031416736

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Parallel Computing Technologies by Victor Malyshkin Pdf

This book constitutes the refereed proceedings of the 17th International Conference on Parallel Computing Technologies, PaCT 2023, held in Astana, Kazakhstan, during August 21-25, 2023. The 15 full papers included in this book were carefully reviewed and selected from 23 submissions. They were organized in topical sections as follows: automatic programming and program tuning; frameworks and services; algorithms; and distributed systems management.

Hands-On Graph Analytics with Neo4j

Author : Estelle Scifo
Publisher : Packt Publishing Ltd
Page : 496 pages
File Size : 49,9 Mb
Release : 2020-08-21
Category : Computers
ISBN : 9781839215667

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Hands-On Graph Analytics with Neo4j by Estelle Scifo Pdf

Discover how to use Neo4j to identify relationships within complex and large graph datasets using graph modeling, graph algorithms, and machine learning Key FeaturesGet up and running with graph analytics with the help of real-world examplesExplore various use cases such as fraud detection, graph-based search, and recommendation systemsGet to grips with the Graph Data Science library with the help of examples, and use Neo4j in the cloud for effective application scalingBook Description Neo4j is a graph database that includes plugins to run complex graph algorithms. The book starts with an introduction to the basics of graph analytics, the Cypher query language, and graph architecture components, and helps you to understand why enterprises have started to adopt graph analytics within their organizations. You’ll find out how to implement Neo4j algorithms and techniques and explore various graph analytics methods to reveal complex relationships in your data. You’ll be able to implement graph analytics catering to different domains such as fraud detection, graph-based search, recommendation systems, social networking, and data management. You’ll also learn how to store data in graph databases and extract valuable insights from it. As you become well-versed with the techniques, you’ll discover graph machine learning in order to address simple to complex challenges using Neo4j. You will also understand how to use graph data in a machine learning model in order to make predictions based on your data. Finally, you’ll get to grips with structuring a web application for production using Neo4j. By the end of this book, you’ll not only be able to harness the power of graphs to handle a broad range of problem areas, but you’ll also have learned how to use Neo4j efficiently to identify complex relationships in your data. What you will learnBecome well-versed with Neo4j graph database building blocks, nodes, and relationshipsDiscover how to create, update, and delete nodes and relationships using Cypher queryingUse graphs to improve web search and recommendationsUnderstand graph algorithms such as pathfinding, spatial search, centrality, and community detectionFind out different steps to integrate graphs in a normal machine learning pipelineFormulate a link prediction problem in the context of machine learningImplement graph embedding algorithms such as DeepWalk, and use them in Neo4j graphsWho this book is for This book is for data analysts, business analysts, graph analysts, and database developers looking to store and process graph data to reveal key data insights. This book will also appeal to data scientists who want to build intelligent graph applications catering to different domains. Some experience with Neo4j is required.

Python Data Science Essentials

Author : Alberto Boschetti,Luca Massaron
Publisher : Packt Publishing Ltd
Page : 373 pages
File Size : 51,6 Mb
Release : 2016-10-28
Category : Computers
ISBN : 9781786462831

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Python Data Science Essentials by Alberto Boschetti,Luca Massaron Pdf

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Python for Graph and Network Analysis

Author : Mohammed Zuhair Al-Taie,Seifedine Kadry
Publisher : Springer
Page : 203 pages
File Size : 55,9 Mb
Release : 2017-03-20
Category : Computers
ISBN : 9783319530048

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Python for Graph and Network Analysis by Mohammed Zuhair Al-Taie,Seifedine Kadry Pdf

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.

Geospatial Data Science Quick Start Guide

Author : Abdishakur Hassan,Jayakrishnan Vijayaraghavan
Publisher : Packt Publishing Ltd
Page : 165 pages
File Size : 44,6 Mb
Release : 2019-05-31
Category : Computers
ISBN : 9781789809336

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Geospatial Data Science Quick Start Guide by Abdishakur Hassan,Jayakrishnan Vijayaraghavan Pdf

Discover the power of location data to build effective, intelligent data models with Geospatial ecosystems Key FeaturesManipulate location-based data and create intelligent geospatial data modelsBuild effective location recommendation systems used by popular companies such as UberA hands-on guide to help you consume spatial data and parallelize GIS operations effectivelyBook Description Data scientists, who have access to vast data streams, are a bit myopic when it comes to intrinsic and extrinsic location-based data and are missing out on the intelligence it can provide to their models. This book demonstrates effective techniques for using the power of data science and geospatial intelligence to build effective, intelligent data models that make use of location-based data to give useful predictions and analyses. This book begins with a quick overview of the fundamentals of location-based data and how techniques such as Exploratory Data Analysis can be applied to it. We then delve into spatial operations such as computing distances, areas, extents, centroids, buffer polygons, intersecting geometries, geocoding, and more, which adds additional context to location data. Moving ahead, you will learn how to quickly build and deploy a geo-fencing system using Python. Lastly, you will learn how to leverage geospatial analysis techniques in popular recommendation systems such as collaborative filtering and location-based recommendations, and more. By the end of the book, you will be a rockstar when it comes to performing geospatial analysis with ease. What you will learnLearn how companies now use location dataSet up your Python environment and install Python geospatial packagesVisualize spatial data as graphsExtract geometry from spatial dataPerform spatial regression from scratchBuild web applications which dynamically references geospatial dataWho this book is for Data Scientists who would like to leverage location-based data and want to use location-based intelligence in their data models will find this book useful. This book is also for GIS developers who wish to incorporate data analysis in their projects. Knowledge of Python programming and some basic understanding of data analysis are all you need to get the most out of this book.

Think Complexity

Author : Allen Downey
Publisher : "O'Reilly Media, Inc."
Page : 159 pages
File Size : 48,5 Mb
Release : 2012-03-02
Category : Computers
ISBN : 9781449314637

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Think Complexity by Allen Downey Pdf

Enhances Python skills by working with data structures and algorithms and gives examples of complex systems using exercises, case studies, and simple explanations.

Handbook of Graphs and Networks in People Analytics

Author : Keith McNulty
Publisher : CRC Press
Page : 269 pages
File Size : 41,5 Mb
Release : 2022-06-19
Category : Business & Economics
ISBN : 9781000597233

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Handbook of Graphs and Networks in People Analytics by Keith McNulty Pdf

Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.