The 9 best social networks in r

Finding your suitable social networks in r is not easy. You may need consider between hundred or thousand products from many store. In this article, we make a short list of the best social networks in r including detail information and customer reviews. Let’s find out which is your favorite one.

Best social networks in r

Product	Features	Editor's score	Go to site
	System Dynamics Modeling with R (Lecture Notes in Social Networks)		Go to amazon.com
	Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)		Go to amazon.com
	Network Analysis and Visualization in R: Quick Start Guide		Go to amazon.com
	Statistical Analysis of Network Data with R (Use R!)		Go to amazon.com
	Research Methods in Social Network Analysis		Go to amazon.com
	Diffusion Source Localization in Large Networks (Synthesis Lectures on Communication Networks)		Go to amazon.com
	Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)		Go to amazon.com
	Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences)		Go to amazon.com
	Relational, Networked and Collaborative Approaches to Public Diplomacy: The Connective Mindshift (Routledge Studies in Global Information, Politics and Society)		Go to amazon.com

Related posts:

1. System Dynamics Modeling with R (Lecture Notes in Social Networks)

Description

This new interdisciplinary work presents system dynamics as a powerful approach to enable analysts build simulation models of social systems, with a view toward enhancing decision making. Grounded in the feedback perspective of complex systems, the book provides a practical introduction to system dynamics, and covers key concepts such as stocks, flows, and feedback. Societal challenges such as predicting the impact of an emerging infectious disease, estimating population growth, and assessing the capacity of health services to cope with demographic change can all benefit from the application of computer simulation. This text explains important building blocks of the system dynamics approach, including material delays, stock management heuristics, and how to model effects between different systemic elements. Models from epidemiology, health systems, and economics are presented to illuminate important ideas, and the R programming language is used to provide an open-source and interoperable way to build system dynamics models. System Dynamics Modeling with R also describes hands-on techniques that can enhance client confidence in system dynamic models, including model testing, model analysis, and calibration. Developed from the authors course in system dynamics, this book is written for undergraduate and postgraduate students of management, operations research, computer science, and applied mathematics. Its focus is on the fundamental building blocks of system dynamics models, and its choice of R as a modeling language make it an ideal reference text for those wishing to integrate system dynamics modeling with related data analytic methods and techniques.

2. Humanities Data in R: Exploring Networks, Geospatial Data, Images, and Text (Quantitative Methods in the Humanities and Social Sciences)

Description

This pioneering book teaches readers to use R within four core analytical areas applicable to the Humanities: networks, text, geospatial data, andimages. This book is also designed to be a bridge: between quantitative and qualitative methods, individual and collaborative work, and the humanities and social sciences. Humanities Data with R does not presuppose backgroundprogramming experience. Early chapters take readers from R set-up to exploratory data analysis (continuous and categorical data, multivariate analysis, and advanced graphics with emphasis on aesthetics and facility). Following this, networks, geospatial data, image data, natural language processing and text analysis each have a dedicated chapter. Each chapter is grounded in examples to move readers beyond the intimidation of adding new tools to their research. Everything is hands-on: networks are explained using U.S. Supreme Court opinions, and low-level NLP methods are applied to short stories by Sir Arthur Conan Doyle. After working through these examples with the provided data, code and book website, readers are prepared to apply new methods to their own work. The open source R programming language, with its myriad packages and popularity within the sciences and social sciences, is particularly well-suited to working with humanities data. R packages are also highlighted in an appendix. This book uses an expanded conception of the forms data may take and the information it represents. The methodology will have wide application in classrooms and self-study for the humanities, but also for use in linguistics, anthropology, and political science. Outside the classroom, this intersection of humanities and computing is particularly relevant for research and new modes of dissemination across archives, museums and libraries.

3. Network Analysis and Visualization in R: Quick Start Guide

Description

Social network analysis is used to investigate the inter-relationship between entities. Examples of network structures, include: social media networks, friendship networks and collaboration networks. This book provides a quick start guide to network analysis and visualization in R. You'll learn, how to: - Create static and interactive network graphs using modern R packages. - Change the layout of network graphs. - Detect important or central entities in a network graph. - Detect community (or cluster) in a network.

4. Statistical Analysis of Network Data with R (Use R!)

Feature

Springer

Description

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. Kolaczyks book Statistical Analysis of Network Data (Springer, 2009).

5. Research Methods in Social Network Analysis

Description

Since the publication of Herbert Spencer's Principles of Sociology in 1875, the use of social structure as a defining concept has produced a large body of creative speculations, insights, and intuitions about social life. However, writers in this tradition do not always provide the sorts of formal definitons and propositions that are the building blocks of modern social research. In its broad-ranging examination of the kind of data that form the basis for the systematic study of social structure, Research Methods in Social Network Analysis marks a significant methodological advance in network studies.

As used in this volume, social structure refers to a bundle of intuitive natural language ideas and concepts about patterning in social relationships among people. In contrast, social networks is used to refer to a collection of precise analytic and methodological concepts and procedures that facilitate the collection of data and the systematic study of such patterning. Accordingly, the book's five sections are arranged to address analytical problems in a series of logically ordered stages or processes.

The major contributors define the fundamental modes by which social structural phenomena are to be represented; how boundaries to a social structure are set; how the relations of a network are measured in terms of structure and content; the ways in which the relational structure of a network affects system actors; and how actors within a social network are clustered into cliques or groups. The chapters in the last section build on solutions to problems proposed in the previous sections. This highly unified approach to research design combined with a representative diversity of viewpoints makes Research Methods in Social Network Analysis a state-of-the-art volume.

6. Diffusion Source Localization in Large Networks (Synthesis Lectures on Communication Networks)

Description

Diffusion processes in large networks have been used to model many real-world phenomena, including how rumors spread on the Internet, epidemics among human beings, emotional contagion through social networks, and even gene regulatory processes. Fundamental estimation principles and efficient algorithms for locating diffusion sources can answer a wide range of important questions, such as identifying the source of a widely spread rumor on online social networks. This book provides an overview of recent progress on source localization in large networks, focusing on theoretical principles and fundamental limits. The book covers both discrete-time diffusion models and continuous-time diffusion models. For discrete-time diffusion models, the book focuses on the Jordan infection center; for continuous-time diffusion models, it focuses on the rumor center. Most theoretical results on source localization are based on these two types of estimators or their variants. This book also includes algorithms that leverage partial-time information for source localization and a brief discussion of interesting unresolved problems in this area.

7. Marketing Data Science: Modeling Techniques in Predictive Analytics with R and Python (FT Press Analytics)

Feature

Pearson FT Press

Description

Now , a leader of Northwestern University's prestigious analytics program presents a fully-integrated treatment of both the business and academic elements of marketing applications in predictive analytics. Writing for both managers and students, Thomas W. Miller explains essential concepts, principles, and theory in the context of real-world applications.

Building on Miller's pioneering program, Marketing Data Science thoroughly addresses segmentation, target marketing, brand and product positioning, new product development, choice modeling, recommender systems, pricing research, retail site selection, demand estimation, sales forecasting, customer retention, and lifetime value analysis.

Starting where Miller's widely-praised Modeling Techniques in Predictive Analytics left off, he integrates crucial information and insights that were previously segregated in texts on web analytics, network science, information technology, and programming. Coverage includes:

• The role of analytics in delivering effective messages on the web
• Understanding the web by understanding its hidden structures
• Being recognized on the web and watching your own competitors
• Visualizing networks and understanding communities within them
• Measuring sentiment and making recommendations
• Leveraging key data science methods: databases/data preparation, classical/Bayesian statistics, regression/classification, machine learning, and text analytics

Six complete case studies address exceptionally relevant issues such as: separating legitimate email from spam; identifying legally-relevant information for lawsuit discovery; gleaning insights from anonymous web surfing data, and more. This text's extensive set of web and network problems draw on rich public-domain data sources; many are accompanied by solutions in Python and/or R.

Marketing Data Science will be an invaluable resource for all students, faculty, and professional marketers who want to use business analytics to improve marketing performance.

8. Social Network Analysis: Methods and Applications (Structural Analysis in the Social Sciences)

Feature

Used Book in Good Condition

Description

Social network analysis, which focuses on relationships among social entities, is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. Social Network Analysis: Methods and Applications reviews and discusses methods for the analysis of social networks with a focus on applications of these methods to many substantive examples. As the first book to provide a comprehensive coverage of the methodology and applications of the field, this study is both a reference book and a textbook.

9. Relational, Networked and Collaborative Approaches to Public Diplomacy: The Connective Mindshift (Routledge Studies in Global Information, Politics and Society)

Description

Over the past decade, scholars, practitioners, and leading diplomats have forcefully argued for the need to move beyond one-way, mass-media-driven campaigns and develop more relational strategies. In the coming years, as the range of public diplomacy actors grows, the issues become more complexly intertwined, and the use of social media proliferates, the focus on relations will intensify along with the demands for more sophisticated strategies. These changes in the international arena call for a connective mindshift: a shift from information control and dominance to skilled relationship management.

Leading international scholars and practitioners embark on a forward-looking exploration of creative conceptual frameworks, training methods, and case studies that advance relational, networking, and collaborative strategies in public diplomacy. Light on academic jargon and rich in analysis, this volume argues that while relationships have always been pivotal to the practice of public diplomacy, the relational dynamics are changing. Rather than focus on specific definitions, the contributors focus on the dynamic interplay of influence in the public diplomacy environment. That environment includes state and non-state actors, public and private partners, competitors and collaborators, new and old media, and is conditioned by power, ethics, and cultures.

This book is an essential resource to students and practitioners interested on how to build relationships and transform them into more elaborate network structures through public communication. It will challenge you to push the boundaries of what you think are the mechanisms, benefits, and potential issues raised by a relational approach to public diplomacy

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