Social Network Analytics

Social Network Analytics
Author: Nilanjan Dey,Samarjeet Borah,Rosalina Babo,Amira S. Ashour
Publsiher: Academic Press
Total Pages: 267
Release: 2018-11-16
ISBN 10: 0128156414
ISBN 13: 9780128156414
Language: EN, FR, DE, ES & NL

Social Network Analytics Book Review:

Social Network Analytics: Computational Research Methods and Techniques focuses on various technical concepts and aspects of social network analysis. The book features the latest developments and findings in this emerging area of research. In addition, it includes a variety of applications from several domains, such as scientific research, and the business and industrial sectors. The technical aspects of analysis are covered in detail, including visualizing and modeling, network theory, mathematical models, the big data analytics of social networks, multidimensional scaling, and more. As analyzing social network data is rapidly gaining interest in the scientific research community because of the importance of the information and insights that can be culled from the wealth of data inherent in the various aspects of the network, this book provides insights on measuring the relationships and flows between people, groups, organizations, computers, URLs, and more. Examines a variety of data analytic techniques that can be applied to social networks Discusses various methods of visualizing, modeling and tracking network patterns, organization, growth and change Covers the most recent research on social network analysis and includes applications to a number of domains

Social Network Data Analytics

Social Network Data Analytics
Author: Charu C. Aggarwal
Publsiher: Springer Science & Business Media
Total Pages: 502
Release: 2011-03-18
ISBN 10: 1441984623
ISBN 13: 9781441984623
Language: EN, FR, DE, ES & NL

Social Network Data Analytics Book Review:

Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Social Network Analysis

Social Network Analysis
Author: Stanley Wasserman,Stanley (University of Illinois Wasserman, Urbana-Champaign),Katherine Faust
Publsiher: Cambridge University Press
Total Pages: 825
Release: 1994-11-25
ISBN 10: 9780521387071
ISBN 13: 0521387078
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

Social network analysis is used widely in the social and behavioral sciences, as well as in economics, marketing, and industrial engineering. The social network perspective focuses on relationships among social entities and is an important addition to standard social and behavioral research, which is primarily concerned with attributes of the social units. 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. It is a reference book that can be used by those who want a comprehensive review of network methods, or by researchers who have gathered network data and want to find the most appropriate method by which to analyze it. It is also intended for use as a textbook as it is the first book to provide comprehensive coverage of the methodology and applications of the field.

Exam Prep for Social Network Analytics

Exam Prep for  Social Network Analytics
Author: Anonim
Publsiher: Unknown
Total Pages: 329
Release: 2021
ISBN 10:
ISBN 13:
Language: EN, FR, DE, ES & NL

Exam Prep for Social Network Analytics Book Review:

Social Network Analysis for Startups

Social Network Analysis for Startups
Author: Maksim Tsvetovat,Alexander Kouznetsov
Publsiher: "O'Reilly Media, Inc."
Total Pages: 190
Release: 2011-10-06
ISBN 10: 1449306462
ISBN 13: 9781449306465
Language: EN, FR, DE, ES & NL

Social Network Analysis for Startups Book Review:

SNA techniques are derived from sociological and social-psychological theories and take into account the whole network (or, in case of very large networks such as Twitter -- a large segment of the network). Thus, we may arrive at results that may seem counter-intuitive -- e.g. that Jusin Bieber (7.5 mil. followers) and Lady Gaga (7.2 mil. followers) have relatively little actual influence despite their celebrity status -- while a middle-of-the-road blogger with 30K followers is able to generate tweets that "go viral" and result in millions of impressions. O'Reilly's "Mining Social Media" and "Programming Collective Intelligence" books are an excellent start for people inteseted in SNA. This book builds on these books' foundations to teach a new, pragmatic, way of doing SNA. I would like to write a book that links theory ("why is this important?", "how do various concepts interact?", "how do I interpret quantitative results?") and practice -- gathering, analyzing and visualizing data using Python and other open-source tools.

Sentiment Analysis in Social Networks

Sentiment Analysis in Social Networks
Author: Federico Alberto Pozzi,Elisabetta Fersini,Enza Messina,Bing Liu
Publsiher: Morgan Kaufmann
Total Pages: 284
Release: 2016-10-06
ISBN 10: 0128044381
ISBN 13: 9780128044384
Language: EN, FR, DE, ES & NL

Sentiment Analysis in Social Networks Book Review:

The aim of Sentiment Analysis is to define automatic tools able to extract subjective information from texts in natural language, such as opinions and sentiments, in order to create structured and actionable knowledge to be used by either a decision support system or a decision maker. Sentiment analysis has gained even more value with the advent and growth of social networking. Sentiment Analysis in Social Networks begins with an overview of the latest research trends in the field. It then discusses the sociological and psychological processes underling social network interactions. The book explores both semantic and machine learning models and methods that address context-dependent and dynamic text in online social networks, showing how social network streams pose numerous challenges due to their large-scale, short, noisy, context- dependent and dynamic nature. Further, this volume: Takes an interdisciplinary approach from a number of computing domains, including natural language processing, machine learning, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network analysis Shows how to apply sentiment analysis tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics Takes an interdisciplinary approach from a number of computing domains, including natural language processing, big data, and statistical methodologies Provides insights into opinion spamming, reasoning, and social network mining Shows how to apply opinion mining tools for a particular application and domain, and how to get the best results for understanding the consequences Serves as a one-stop reference for the state-of-the-art in social media analytics

Learning Social Media Analytics with R

Learning Social Media Analytics with R
Author: Raghav Bali,Dipanjan Sarkar,Tushar Sharma
Publsiher: Packt Publishing Ltd
Total Pages: 394
Release: 2017-05-26
ISBN 10: 1787125467
ISBN 13: 9781787125469
Language: EN, FR, DE, ES & NL

Learning Social Media Analytics with R Book Review:

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book A practical guide written to help leverage the power of the R eco-system to extract, process, analyze, visualize and model social media data Learn about data access, retrieval, cleaning, and curation methods for data originating from various social media platforms. Visualize and analyze data from social media platforms to understand and model complex relationships using various concepts and techniques such as Sentiment Analysis, Topic Modeling, Text Summarization, Recommendation Systems, Social Network Analysis, Classification, and Clustering. Who This Book Is For It is targeted at IT professionals, Data Scientists, Analysts, Developers, Machine Learning Enthusiasts, social media marketers and anyone with a keen interest in data, analytics, and generating insights from social data. Some background experience in R would be helpful, but not necessary, since this book is written keeping in mind, that readers can have varying levels of expertise. What You Will Learn Learn how to tap into data from diverse social media platforms using the R ecosystem Use social media data to formulate and solve real-world problems Analyze user social networks and communities using concepts from graph theory and network analysis Learn to detect opinion and sentiment, extract themes, topics, and trends from unstructured noisy text data from diverse social media channels Understand the art of representing actionable insights with effective visualizations Analyze data from major social media channels such as Twitter, Facebook, Flickr, Foursquare, Github, StackExchange, and so on Learn to leverage popular R packages such as ggplot2, topicmodels, caret, e1071, tm, wordcloud, twittR, Rfacebook, dplyr, reshape2, and many more In Detail The Internet has truly become humongous, especially with the rise of various forms of social media in the last decade, which give users a platform to express themselves and also communicate and collaborate with each other. This book will help the reader to understand the current social media landscape and to learn how analytics can be leveraged to derive insights from it. This data can be analyzed to gain valuable insights into the behavior and engagement of users, organizations, businesses, and brands. It will help readers frame business problems and solve them using social data. The book will also cover several practical real-world use cases on social media using R and its advanced packages to utilize data science methodologies such as sentiment analysis, topic modeling, text summarization, recommendation systems, social network analysis, classification, and clustering. This will enable readers to learn different hands-on approaches to obtain data from diverse social media sources such as Twitter and Facebook. It will also show readers how to establish detailed workflows to process, visualize, and analyze data to transform social data into actionable insights. Style and approach This book follows a step-by-step approach with detailed strategies for understanding, extracting, analyzing, visualizing, and modeling data from several major social network platforms such as Facebook, Twitter, Foursquare, Flickr, Github, and StackExchange. The chapters cover several real-world use cases and leverage data science, machine learning, network analysis, and graph theory concepts along with the R ecosystem, including popular packages such as ggplot2, caret,dplyr, topicmodels, tm, and so on.

Social Network Analysis

Social Network Analysis
Author: Song Yang,Franziska B. Keller,Lu Zheng
Publsiher: SAGE Publications
Total Pages: 248
Release: 2016-10-28
ISBN 10: 1506362125
ISBN 13: 9781506362120
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

Social Network Analysis: Methods and Examples by Song Yang, Franziska B. Keller, and Lu Zheng prepares social science students to conduct their own social network analysis (SNA) by covering basic methodological tools along with illustrative examples from various fields. This innovative book takes a conceptual rather than a mathematical approach as it discusses the connection between what SNA methods have to offer and how those methods are used in research design, data collection, and analysis. Four substantive applications chapters provide examples from politics, work and organizations, mental and physical health, and crime and terrorism studies.

Social Media Analytics for User Behavior Modeling

Social Media Analytics for User Behavior Modeling
Author: Arun Reddy Nelakurthi,Jingrui He
Publsiher: CRC Press
Total Pages: 98
Release: 2020-01-21
ISBN 10: 1000025365
ISBN 13: 9781000025361
Language: EN, FR, DE, ES & NL

Social Media Analytics for User Behavior Modeling Book Review:

In recent years social media has gained significant popularity and has become an essential medium of communication. Such user-generated content provides an excellent scenario for applying the metaphor of mining any information. Transfer learning is a research problem in machine learning that focuses on leveraging the knowledge gained while solving one problem and applying it to a different, but related problem. Features: Offers novel frameworks to study user behavior and for addressing and explaining task heterogeneity Presents a detailed study of existing research Provides convergence and complexity analysis of the frameworks Includes algorithms to implement the proposed research work Covers extensive empirical analysis Social Media Analytics for User Behavior Modeling: A Task Heterogeneity Perspective is a guide to user behavior modeling in heterogeneous settings and is of great use to the machine learning community.

Creating Value with Social Media Analytics

Creating Value with Social Media Analytics
Author: Gohar F. Khan
Publsiher: Createspace Independent Publishing Platform
Total Pages: 510
Release: 2018-04-23
ISBN 10: 9781977543974
ISBN 13: 1977543979
Language: EN, FR, DE, ES & NL

Creating Value with Social Media Analytics Book Review:

Often termed as the ''new gold,'' the vast amount of social media data can be employed to identify which customer behavior and actions create more value. Nevertheless, many brands find it extremely hard to define what the value of social media is and how to capture and create value with social media data.In Creating Value with Social Media Analytics, we draw on developments in social media analytics theories and tools to develop a comprehensive social media value creation framework that allows readers to define, align, capture, and sustain value through social media data. The book offers concepts, strategies, tools, tutorials, and case studies that brands need to align, extract, and analyze a variety of social media data, including text, actions, networks, multimedia, apps, hyperlinks, search engines, and location data. By the end of this book, the readers will have mastered the theories, concepts, strategies, techniques, and tools necessary to extract business value from big social media that help increase brand loyalty, generate leads, drive traffic, and ultimately make sound business decisions. Here is how the book is organized. Chapter 1: Creating Value with Social Media Analytics Chapter 2: Understanding Social Media Chapter 3: Understanding Social Media Analytics Chapter 4: Analytics-Business Alignment Chapter 5: Capturing Value with Network Analytics Chapter 6: Capturing Value with Text Analytics Chapter 7: Capturing Value with Actions Analytics Chapter 8: Capturing Value with Search Engine Analytics Chapter 9: Capturing Value with Location Analytics Chapter 10: Capturing Value with Hyperlinks Analytics Chapter 11: Capturing Value with Mobile Analytics Chapter 12: Capturing Value with Multimedia Analytics Chapter 13: Social Media Analytics CapabilitiesChapter 14: Social Media Security, Privacy, & Ethics The book has a companion site (https://analytics-book.com/), which offers useful instructor resources. Praises for the book "Gohar F. Khan has a flair for simplifying the complexity of social media analytics. Creating Value with Social Media Analytics is a beautifully delineated roadmap to creating and capturing business value through social media. It provides the theories, tools, and creates a roadmap to leveraging social media data for business intelligence purposes. Real world analytics cases and tutorials combined with a comprehensive companion site make this an excellent textbook for both graduate and undergraduate students."-Robin Saunders, Director of the Communications and Information Management Graduate Programs, Bay Path University. "Creating Value with Social Media Analytics offers a comprehensive framework to define, align, capture, and sustain business value through social media data. The book is theoretically grounded and practical, making it an excellent resource for social media analytics courses."-Haya Ajjan, Director & Associate Prof., Elon Center for Organizational Analytics, Elon University. "Gohar Khan is a pioneer in the emerging domain of social media analytics. This latest text is a must-read for business leaders, managers, and academicians, as it provides a clear and concise understanding of business value creation with social media data from a social lens."-Laeeq Khan, Director, Social Media Analytics Research Team, Ohio University. "Whether you are coming from a business, research, science or art background, Creating Value with Social Media Analytics is a brilliant induction resource for those entering the social media analytics industry. The insightful case studies and carefully crafted tutorials are the perfect supplements to help digest the key concepts introduced in each chapter."-Jared Wong, Social Media Data Analyst, Digivizer "It is one of the most comprehensive books on analytics that I have come across recently."-Bobby Swar, Prof. Concordia Uni.

Social Network Analysis with Applications

Social Network Analysis with Applications
Author: Ian McCulloh,Helen Armstrong,Anthony Johnson
Publsiher: John Wiley & Sons
Total Pages: 320
Release: 2013-07-01
ISBN 10: 1118644689
ISBN 13: 9781118644683
Language: EN, FR, DE, ES & NL

Social Network Analysis with Applications Book Review:

A comprehensive introduction to social network analysis that hones in on basic centrality measures, social links, subgroup analysis, data sources, and more Written by military, industry, and business professionals, this book introduces readers to social network analysis, the new and emerging topic that has recently become of significant use for industry, management, law enforcement, and military practitioners for identifying both vulnerabilities and opportunities in collaborative networked organizations. Focusing on models and methods for the analysis of organizational risk, Social Network Analysis with Applications provides easily accessible, yet comprehensive coverage of network basics, centrality measures, social link theory, subgroup analysis, relational algebra, data sources, and more. Examples of mathematical calculations and formulas for social network measures are also included. Along with practice problems and exercises, this easily accessible book covers: The basic concepts of networks, nodes, links, adjacency matrices, and graphs Mathematical calculations and exercises for centrality, the basic measures of degree, betweenness, closeness, and eigenvector centralities Graph-level measures, with a special focus on both the visual and numerical analysis of networks Matrix algebra, outlining basic concepts such as matrix addition, subtraction, multiplication, and transpose and inverse calculations in linear algebra that are useful for developing networks from relational data Meta-networks and relational algebra, social links, diffusion through networks, subgroup analysis, and more An excellent resource for practitioners in industry, management, law enforcement, and military intelligence who wish to learn and apply social network analysis to their respective fields, Social Network Analysis with Applications is also an ideal text for upper-level undergraduate and graduate level courses and workshops on the subject.

Social Network Based Big Data Analysis and Applications

Social Network Based Big Data Analysis and Applications
Author: Mehmet Kaya,Jalal Kawash,Suheil Khoury,Min-Yuh Day
Publsiher: Springer
Total Pages: 249
Release: 2018-05-10
ISBN 10: 3319781960
ISBN 13: 9783319781969
Language: EN, FR, DE, ES & NL

Social Network Based Big Data Analysis and Applications Book Review:

This book is a timely collection of chapters that present the state of the art within the analysis and application of big data. Working within the broader context of big data, this text focuses on the hot topics of social network modelling and analysis such as online dating recommendations, hiring practices, and subscription-type prediction in mobile phone services. Manuscripts are expanded versions of the best papers presented at the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM’2016), which was held in August 2016. The papers were among the best featured at the meeting and were then improved and extended substantially. Social Network Based Big Data Analysis and Applications will appeal to students and researchers in the field.

State of the Art Applications of Social Network Analysis

State of the Art Applications of Social Network Analysis
Author: Fazli Can,Tansel Özyer,Faruk Polat
Publsiher: Springer
Total Pages: 372
Release: 2014-05-14
ISBN 10: 3319059122
ISBN 13: 9783319059129
Language: EN, FR, DE, ES & NL

State of the Art Applications of Social Network Analysis Book Review:

Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user’s- behavior, privacy in social network analysis, mobility from spatio-temporal point of view, agent technology and political parties in parliament. These topics will be of interest to researchers and practitioners from different disciplines including, but not limited to, social sciences and engineering.

Mixed Methods Social Network Analysis

Mixed Methods Social Network Analysis
Author: Dominik E. Froehlich,Martin Rehm,Bart C. Rienties
Publsiher: Routledge
Total Pages: 280
Release: 2019-12-09
ISBN 10: 0429557043
ISBN 13: 9780429557040
Language: EN, FR, DE, ES & NL

Mixed Methods Social Network Analysis Book Review:

Mixed Methods Social Network Analysis brings together diverse perspectives from 42 international experts on how to design, implement, and evaluate mixed methods social network analysis (MMSNA). There is an increased recognition that social networks can be important catalysts for change and transformation. This edited book from leading experts in mixed methods and social network analysis describes how researchers can conceptualize, develop, mix, and intersect diverse approaches, concepts, and tools. In doing so, they can improve their understanding and insights into the complex change processes in social networks. Section 1 includes eight chapters that reflect on "Why should we do MMSNA?", providing a clear map of MMSNA research to date and why to consider MMSNA. In Section 2 the remaining 11 chapters are dedicated to the question "How do I do MMSNA?", illustrating how concentric circles, learning analytics, qualitative structured approaches, relational event modeling, and other approaches can empower researchers. This book shows that mixing qualitative and quantitative approaches to social network analysis can empower people to understand the complexities of change in networks and relations between people. It shows how mixed analysis can be applied to a wide range of data generated by diverse global communities: American school children, Belgian teachers, Dutch medical professionals, Finnish consultants, French school children, and Swedish right-wing social media users, amongst others. It will be of great interest to researchers and postgraduate students in education and social sciences and mixed methods scholars.

Models and Methods in Social Network Analysis

Models and Methods in Social Network Analysis
Author: Peter J. Carrington,John Scott,Stanley Wasserman
Publsiher: Cambridge University Press
Total Pages: 329
Release: 2005-02-07
ISBN 10: 9781139443432
ISBN 13: 1139443437
Language: EN, FR, DE, ES & NL

Models and Methods in Social Network Analysis Book Review:

Models and Methods in Social Network Analysis, first published in 2005, presents the most important developments in quantitative models and methods for analyzing social network data that have appeared during the 1990s. Intended as a complement to Wasserman and Faust's Social Network Analysis: Methods and Applications, it is a collection of articles by leading methodologists reviewing advances in their particular areas of network methods. Reviewed are advances in network measurement, network sampling, the analysis of centrality, positional analysis or blockmodelling, the analysis of diffusion through networks, the analysis of affiliation or 'two-mode' networks, the theory of random graphs, dependence graphs, exponential families of random graphs, the analysis of longitudinal network data, graphical techniques for exploring network data, and software for the analysis of social networks.

Fraud Analytics Using Descriptive Predictive and Social Network Techniques

Fraud Analytics Using Descriptive  Predictive  and Social Network Techniques
Author: Bart Baesens,Wouter Verbeke,Veronique Van Vlasselaer
Publsiher: John Wiley & Sons
Total Pages: 400
Release: 2015-08-17
ISBN 10: 1119133122
ISBN 13: 9781119133124
Language: EN, FR, DE, ES & NL

Fraud Analytics Using Descriptive Predictive and Social Network Techniques Book Review:

Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, model building, and post–implementation, with comprehensive guidance on various learning techniques and the data types utilized by each. These techniques are effective for fraud detection across industry boundaries, including applications in insurance fraud, credit card fraud, anti–money laundering, healthcare fraud, telecommunications fraud, click fraud, tax evasion, and more, giving you a highly practical framework for fraud prevention. It is estimated that a typical organization loses about 5% of its revenue to fraud every year. More effective fraud detection is possible, and this book describes the various analytical techniques your organization must implement to put a stop to the revenue leak. Examine fraud patterns in historical data Utilize labeled, unlabeled, and networked data Detect fraud before the damage cascades Reduce losses, increase recovery, and tighten security The longer fraud is allowed to go on, the more harm it causes. It expands exponentially, sending ripples of damage throughout the organization, and becomes more and more complex to track, stop, and reverse. Fraud prevention relies on early and effective fraud detection, enabled by the techniques discussed here. Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques helps you stop fraud in its tracks, and eliminate the opportunities for future occurrence.

The Influence of Technology on Social Network Analysis and Mining

The Influence of Technology on Social Network Analysis and Mining
Author: Tansel Özyer,Jon Rokne,Gerhard Wagner,Arno H.P. Reuser
Publsiher: Springer Science & Business Media
Total Pages: 643
Release: 2013-03-15
ISBN 10: 3709113466
ISBN 13: 9783709113462
Language: EN, FR, DE, ES & NL

The Influence of Technology on Social Network Analysis and Mining Book Review:

The study of social networks was originated in social and business communities. In recent years, social network research has advanced significantly; the development of sophisticated techniques for Social Network Analysis and Mining (SNAM) has been highly influenced by the online social Web sites, email logs, phone logs and instant messaging systems, which are widely analyzed using graph theory and machine learning techniques. People perceive the Web increasingly as a social medium that fosters interaction among people, sharing of experiences and knowledge, group activities, community formation and evolution. This has led to a rising prominence of SNAM in academia, politics, homeland security and business. This follows the pattern of known entities of our society that have evolved into networks in which actors are increasingly dependent on their structural embedding General areas of interest to the book include information science and mathematics, communication studies, business and organizational studies, sociology, psychology, anthropology, applied linguistics, biology and medicine.

Social Network Analysis

Social Network Analysis
Author: Xiaoming Fu,Jar-Der Luo,Margarete Boos
Publsiher: CRC Press
Total Pages: 396
Release: 2017-03-31
ISBN 10: 1498736688
ISBN 13: 9781498736688
Language: EN, FR, DE, ES & NL

Social Network Analysis Book Review:

The book addresses the issue of interdisciplinary understanding of collaboration on the topic of social network studies. Researchers and practitioners from various disciplines including sociology, computer science, socio-psychology, public health, complex systems, and management science have worked largely independently, each with quite different principles, terminologies, theories. and methodologies. The book aims to fill the gap among these disciplines with a number of the latest interdisciplinary collaboration studies.

Social Network Analytics for Contemporary Business Organizations

Social Network Analytics for Contemporary Business Organizations
Author: Bansal, Himani,Shrivastava, Gulshan,Nguyen, Gia Nhu,Stanciu, Loredana-Mihaela
Publsiher: IGI Global
Total Pages: 321
Release: 2018-03-23
ISBN 10: 1522550984
ISBN 13: 9781522550983
Language: EN, FR, DE, ES & NL

Social Network Analytics for Contemporary Business Organizations Book Review:

Social technology is quickly becoming a vital tool in our personal, educational, and professional lives. Its use must be further examined in order to determine the role of social media technology in organizational settings to promote business development and growth. Social Network Analytics for Contemporary Business Organizations is a critical scholarly resource that analyzes the application of social media in business applications. Featuring coverage on a broad range of topics, such as business management, dynamic networks, and online interaction, this book is geared towards professionals, researchers, academics, students, managers, and practitioners actively involved in the business industry.

Graph Based Social Media Analysis

Graph Based Social Media Analysis
Author: Ioannis Pitas
Publsiher: CRC Press
Total Pages: 424
Release: 2016-04-19
ISBN 10: 1498719058
ISBN 13: 9781498719056
Language: EN, FR, DE, ES & NL

Graph Based Social Media Analysis Book Review:

Focused on the mathematical foundations of social media analysis, Graph-Based Social Media Analysis provides a comprehensive introduction to the use of graph analysis in the study of social and digital media. It addresses an important scientific and technological challenge, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal processing. Supplying an overview of graph-based social media analysis, the book provides readers with a clear understanding of social media structure. It uses graph theory, particularly the algebraic description and analysis of graphs, in social media studies. The book emphasizes the big data aspects of social and digital media. It presents various approaches to storing vast amounts of data online and retrieving that data in real-time. It demystifies complex social media phenomena, such as information diffusion, marketing and recommendation systems in social media, and evolving systems. It also covers emerging trends, such as big data analysis and social media evolution. Describing how to conduct proper analysis of the social and digital media markets, the book provides insights into processing, storing, and visualizing big social media data and social graphs. It includes coverage of graphs in social and digital media, graph and hyper-graph fundamentals, mathematical foundations coming from linear algebra, algebraic graph analysis, graph clustering, community detection, graph matching, web search based on ranking, label propagation and diffusion in social media, graph-based pattern recognition and machine learning, graph-based pattern classification and dimensionality reduction, and much more. This book is an ideal reference for scientists and engineers working in social media and digital media production and distribution. It is also suitable for use as a textbook in undergraduate or graduate courses on digital media, social media, or social networks.