Python For Graph And Network Analysis

Author : Mohammed Zuhair Al-Taie
Publisher : Springer
ISBN 13 : 3319530046
Total Pages : 203 pages
Book Rating : 4.48/5 ( download)

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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 203 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.

Author : Dmitry Zinoviev
Publisher : Pragmatic Bookshelf
ISBN 13 : 1680505408
Total Pages : 343 pages
Book Rating : 4.05/5 ( download)

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Download or read book Complex Network Analysis in Python written by Dmitry Zinoviev and published by Pragmatic Bookshelf. This book was released on 2018-01-19 with total page 343 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Krishna Raj P.M.
Publisher : Springer
ISBN 13 : 3319967460
Total Pages : 329 pages
Book Rating : 4.62/5 ( download)

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Download or read book Practical Social Network Analysis with Python written by Krishna Raj P.M. and published by Springer. This book was released on 2018-08-25 with total page 329 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on social network analysis from a computational perspective, introducing readers to the fundamental aspects of network theory by discussing the various metrics used to measure the social network. It covers different forms of graphs and their analysis using techniques like filtering, clustering and rule mining, as well as important theories like small world phenomenon. It also presents methods for identifying influential nodes in the network and information dissemination models. Further, it uses examples to explain the tools for visualising large-scale networks, and explores emerging topics like big data and deep learning in the context of social network analysis. With the Internet becoming part of our everyday lives, social networking tools are used as the primary means of communication. And as the volume and speed of such data is increasing rapidly, there is a need to apply computational techniques to interpret and understand it. Moreover, relationships in molecular structures, co-authors in scientific journals, and developers in a software community can also be understood better by visualising them as networks. This book brings together the theory and practice of social network analysis and includes mathematical concepts, computational techniques and examples from the real world to offer readers an overview of this domain.

Author : Keith McNulty
Publisher : CRC Press
ISBN 13 : 1000597237
Total Pages : 269 pages
Book Rating : 4.33/5 ( download)

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Download or read book Handbook of Graphs and Networks in People Analytics written by Keith McNulty and published by CRC Press. This book was released on 2022-06-19 with total page 269 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Maksim Tsvetovat
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1449306462
Total Pages : 191 pages
Book Rating : 4.65/5 ( download)

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Download or read book Social Network Analysis for Startups written by Maksim Tsvetovat and published by "O'Reilly Media, Inc.". This book was released on 2011-10-06 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available. Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you'll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You'll also learn how to use Python and other open source tools—such as NetworkX, NumPy, and Matplotlib—to gather, analyze, and visualize social data. This book is the perfect marriage between social network theory and practice, and a valuable source of insight and ideas. Discover how internal social networks affect a company’s ability to perform Follow terrorists and revolutionaries through the 1998 Khobar Towers bombing, the 9/11 attacks, and the Egyptian uprising Learn how a single special-interest group can control the outcome of a national election Examine relationships between companies through investment networks and shared boards of directors Delve into the anatomy of cultural fads and trends—offline phenomena often mediated by Twitter and Facebook

Author : Edward L. Platt
Publisher : Packt Publishing Ltd
ISBN 13 : 1789950414
Total Pages : 181 pages
Book Rating : 4.10/5 ( download)

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Download or read book Network Science with Python and NetworkX Quick Start Guide written by Edward L. Platt and published by Packt Publishing Ltd. This book was released on 2019-04-26 with total page 181 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : David Knickerbocker
Publisher : Packt Publishing Ltd
ISBN 13 : 1801075212
Total Pages : 414 pages
Book Rating : 4.13/5 ( download)

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Download or read book Network Science with Python written by David Knickerbocker and published by Packt Publishing Ltd. This book was released on 2023-02-28 with total page 414 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Author : Jure Leskovec
Publisher : Cambridge University Press
ISBN 13 : 1107077230
Total Pages : 480 pages
Book Rating : 4.32/5 ( download)

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Download or read book Mining of Massive Datasets written by Jure Leskovec and published by Cambridge University Press. This book was released on 2014-11-13 with total page 480 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now in its second edition, this book focuses on practical algorithms for mining data from even the largest datasets.

Author : Eric D. Kolaczyk
Publisher : Springer
ISBN 13 : 1493909835
Total Pages : 207 pages
Book Rating : 4.34/5 ( download)

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Download or read book Statistical Analysis of Network Data with R written by Eric D. Kolaczyk and published by Springer. This book was released on 2014-05-22 with total page 207 pages. Available in PDF, EPUB and Kindle. Book excerpt: Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

Author : Claudio Stamile
Publisher : Packt Publishing Ltd
ISBN 13 : 1800206755
Total Pages : 338 pages
Book Rating : 4.55/5 ( download)

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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.