Networks Crowds And Markets

Author: David Easley
Editor: Cambridge University Press
ISBN: 9781139490306
Size: 20,13 MB
Format: PDF
Read: 774
Download

Are all film stars linked to Kevin Bacon? Why do the stock markets rise and fall sharply on the strength of a vague rumour? How does gossip spread so quickly? Are we all related through six degrees of separation? There is a growing awareness of the complex networks that pervade modern society. We see them in the rapid growth of the Internet, the ease of global communication, the swift spread of news and information, and in the way epidemics and financial crises develop with startling speed and intensity. This introductory book on the new science of networks takes an interdisciplinary approach, using economics, sociology, computing, information science and applied mathematics to address fundamental questions about the links that connect us, and the ways that our decisions can have consequences for others.

A Course In Networks And Markets

Author: Rafael Pass
Editor: MIT Press
ISBN: 0262039788
Size: 18,73 MB
Format: PDF, ePub, Mobi
Read: 675
Download

A graduate-level, mathematically rigorous introduction to strategic behavior in a networked world. This introductory graduate-level text uses tools from game theory and graph theory to examine the role of network structures and network effects in economic and information markets. The goal is for students to develop an intuitive and mathematically rigorous understanding of how strategic agents interact in a connected world. The text synthesizes some of the central results in the field while also simplifying their treatment to make them more accessible to nonexperts. Thus, students at the introductory level will gain an understanding of key ideas in the field that are usually only taught at the advanced graduate level. The book introduces basic concepts from game theory and graph theory as well as some fundamental algorithms for exploring graphs. These tools are then applied to analyze strategic interactions over social networks, to explore different types of markets and mechanisms for networks, and to study the role of beliefs and higher-level beliefs (beliefs about beliefs). Specific topics discussed include coordination and contagion on social networks, traffic networks, matchings and matching markets, exchange networks, auctions, voting, web search, models of belief and knowledge, and how beliefs affect auctions and markets. An appendix offers a “Primer on Probability.” Mathematically rigorous, the text assumes a level of mathematical maturity (comfort with definitions and proofs) in the reader.

Web And Network Data Science

Author: Thomas W. Miller
Editor: FT Press
ISBN: 0133887642
Size: 11,72 MB
Format: PDF, Mobi
Read: 618
Download

Master modern web and network data modeling: both theory and applications. In Web and Network Data Science, a top faculty member of Northwestern University’s prestigious analytics program presents the first fully-integrated treatment of both the business and academic elements of web and network modeling for predictive analytics. Some books in this field focus either entirely on business issues (e.g., Google Analytics and SEO); others are strictly academic (covering topics such as sociology, complexity theory, ecology, applied physics, and economics). This text gives today's managers and students what they really need: integrated coverage of concepts, principles, and theory in the context of real-world applications. Building on his pioneering Web Analytics course at Northwestern University, Thomas W. Miller covers usability testing, Web site performance, usage analysis, social media platforms, search engine optimization (SEO), and many other topics. He balances this practical coverage with accessible and up-to-date introductions to both social network analysis and network science, demonstrating how these disciplines can be used to solve real business problems.

Mastering Gephi Network Visualization

Author: Ken Cherven
Editor: Packt Publishing Ltd
ISBN: 1783987359
Size: 16,33 MB
Format: PDF, Docs
Read: 243
Download

This book is intended for anyone interested in advanced network analysis. If you wish to master the skills of analyzing and presenting network graphs effectively, then this is the book for you. No coding experience is required to use this book, although some familiarity with the Gephi user interface will be helpful.

Modeling Techniques In Predictive Analytics With Python And R

Author: Thomas W. Miller
Editor: FT Press
ISBN: 013389214X
Size: 13,49 MB
Format: PDF, Mobi
Read: 966
Download

Master predictive analytics, from start to finish Start with strategy and management Master methods and build models Transform your models into highly-effective code—in both Python and R This one-of-a-kind book will help you use predictive analytics, Python, and R to solve real business problems and drive real competitive advantage. You’ll master predictive analytics through realistic case studies, intuitive data visualizations, and up-to-date code for both Python and R—not complex math. Step by step, you’ll walk through defining problems, identifying data, crafting and optimizing models, writing effective Python and R code, interpreting results, and more. Each chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work—and maximize their value. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, addresses everything you need to succeed: strategy and management, methods and models, and technology and code. If you’re new to predictive analytics, you’ll gain a strong foundation for achieving accurate, actionable results. If you’re already working in the field, you’ll master powerful new skills. If you’re familiar with either Python or R, you’ll discover how these languages complement each other, enabling you to do even more. All data sets, extensive Python and R code, and additional examples available for download at http://www.ftpress.com/miller/ Python and R offer immense power in predictive analytics, data science, and big data. This book will help you leverage that power to solve real business problems, and drive real competitive advantage. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, illuminating each technique with carefully explained code for the latest versions of Python and R. If you’re new to predictive analytics, Miller gives you a strong foundation for achieving accurate, actionable results. If you’re already a modeler, programmer, or manager, you’ll learn crucial skills you don’t already have. Using Python and R, Miller addresses multiple business challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic code that delivers actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. Appendices include five complete case studies, and a detailed primer on modern data science methods. Use Python and R to gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Modeling Techniques In Predictive Analytics

Author: Thomas W. Miller
Editor: FT Press
ISBN: 0133886190
Size: 11,13 MB
Format: PDF, Mobi
Read: 370
Download

To succeed with predictive analytics, you must understand it on three levels: Strategy and management Methods and models Technology and code This up-to-the-minute reference thoroughly covers all three categories. Now fully updated, this uniquely accessible book will help you use predictive analytics to solve real business problems and drive real competitive advantage. If you’re new to the discipline, it will give you the strong foundation you need to get accurate, actionable results. If you’re already a modeler, programmer, or manager, it will teach you crucial skills you don’t yet have. Unlike competitive books, this guide illuminates the discipline through realistic vignettes and intuitive data visualizations–not complex math. Thomas W. Miller, leader of Northwestern University’s pioneering program in predictive analytics, guides you through defining problems, identifying data, crafting and optimizing models, writing effective R code, interpreting results, and more. Every chapter focuses on one of today’s key applications for predictive analytics, delivering skills and knowledge to put models to work–and maximize their value. Reflecting extensive student and instructor feedback, this edition adds five classroom-tested case studies, updates all code for new versions of R, explains code behavior more clearly and completely, and covers modern data science methods even more effectively. All data sets, extensive R code, and additional examples available for download at http://www.ftpress.com/miller If you want to make the most of predictive analytics, data science, and big data, this is the book for you. Thomas W. Miller’s unique balanced approach combines business context and quantitative tools, appealing to managers, analysts, programmers, and students alike. Miller addresses multiple business cases and challenges, including segmentation, brand positioning, product choice modeling, pricing research, finance, sports, text analytics, sentiment analysis, and social network analysis. He illuminates the use of cross-sectional data, time series, spatial, and spatio-temporal data. You’ll learn why each problem matters, what data are relevant, and how to explore the data you’ve identified. Miller guides you through conceptually modeling each data set with words and figures; and then modeling it again with realistic R programs that deliver actionable insights. You’ll walk through model construction, explanatory variable subset selection, and validation, mastering best practices for improving out-of-sample predictive performance. Throughout, Miller employs data visualization and statistical graphics to help you explore data, present models, and evaluate performance. This edition adds five new case studies, updates all code for the newest versions of R, adds more commenting to clarify how the code works, and offers a more detailed and up-to-date primer on data science methods. Gain powerful, actionable, profitable insights about: Advertising and promotion Consumer preference and choice Market baskets and related purchases Economic forecasting Operations management Unstructured text and language Customer sentiment Brand and price Sports team performance And much more

Cornell

Author: Glenn C. Altschuler
Editor: Cornell University Press
ISBN: 0801471885
Size: 17,44 MB
Format: PDF, ePub, Docs
Read: 935
Download

In their history of Cornell since 1940, Glenn C. Altschuler and Isaac Kramnick examine the institution in the context of the emergence of the modern research university. The book examines Cornell during the Cold War, the civil rights movement, Vietnam, antiapartheid protests, the ups and downs of varsity athletics, the women's movement, the opening of relations with China, and the creation of Cornell NYC Tech. It relates profound, fascinating, and little-known incidents involving the faculty, administration, and student life, connecting them to the "Cornell idea" of freedom and responsibility. The authors had access to all existing papers of the presidents of Cornell, which deeply informs their respectful but unvarnished portrait of the university. Institutions, like individuals, develop narratives about themselves. Cornell constructed its sense of self, of how it was special and different, on the eve of World War II, when America defended democracy from fascist dictatorship. Cornell’s fifth president, Edmund Ezra Day, and Carl Becker, its preeminent historian, discerned what they called a Cornell "soul," a Cornell "character," a Cornell "personality," a Cornell "tradition"—and they called it "freedom." "The Cornell idea" was tested and contested in Cornell’s second seventy-five years. Cornellians used the ideals of freedom and responsibility as weapons for change—and justifications for retaining the status quo; to protect academic freedom—and to rein in radical professors; to end in loco parentis and parietal rules, to preempt panty raids, pornography, and pot parties, and to reintroduce regulations to protect and promote the physical and emotional well-being of students; to add nanofabrication, entrepreneurship, and genomics to the curriculum—and to require language courses, freshmen writing, and physical education. In the name of freedom (and responsibility), black students occupied Willard Straight Hall, the anti–Vietnam War SDS took over the Engineering Library, proponents of divestment from South Africa built campus shantytowns, and Latinos seized Day Hall. In the name of responsibility (and freedom), the university reclaimed them. The history of Cornell since World War II, Altschuler and Kramnick believe, is in large part a set of variations on the narrative of freedom and its partner, responsibility, the obligation to others and to one’s self to do what is right and useful, with a principled commitment to the Cornell community—and to the world outside the Eddy Street gate.

Reinventing Discovery

Author: Michael Nielsen
Editor: Princeton University Press
ISBN: 0691148902
Size: 19,29 MB
Format: PDF
Read: 135
Download

"Reinventing Discovery argues that we are in the early days of the most dramatic change in how science is done in more than 300 years. This change is being driven by new online tools, which are transforming and radically accelerating scientific discovery"--Provided by publisher.

Motivational Dynamics In Language Learning

Author: Zoltán Dörnyei
Editor: Multilingual Matters
ISBN: 1783092564
Size: 19,50 MB
Format: PDF, Docs
Read: 706
Download

This landmark volume offers a collection of conceptual papers and data-based research studies that investigate the dynamics of language learning motivation from a complex dynamic systems perspective. The chapters seek to answer the question of how we can understand motivation if we perceive it as a continuously changing and evolving entity rather than a fixed learner trait.

Provenance Data In Social Media

Author: Geoffrey Barbier
Editor: Morgan & Claypool Publishers
ISBN: 1608457842
Size: 17,63 MB
Format: PDF, ePub, Docs
Read: 657
Download

Social media shatters the barrier to communicate anytime anywhere for people of all walks of life. The publicly available, virtually free information in social media poses a new challenge to consumers who have to discern whether a piece of information published in social media is reliable. For example, it can be difficult to understand the motivations behind a statement passed from one user to another, without knowing the person who originated the message. Additionally, false information can be propagated through social media, resulting in embarrassment or irreversible damages. Provenance data associated with a social media statement can help dispel rumors, clarify opinions, and confirm facts. However, provenance data about social media statements is not readily available to users today. Currently, providing this data to users requires changing the social media infrastructure or offering subscription services. Taking advantage of social media features, research in this nascent field spearheads the search for a way to provide provenance data to social media users, thus leveraging social media itself by mining it for the provenance data. Searching for provenance data reveals an interesting problem space requiring the development and application of new metrics in order to provide meaningful provenance data to social media users. This lecture reviews the current research on information provenance, explores exciting research opportunities to address pressing needs, and shows how data mining can enable a social media user to make informed judgements about statements published in social media. Table of Contents: Information Provenance in Social Media / Provenance Attributes / Provenance via Network Information / Provenance Data