Social network analytics computational research methods and techniques / [electronic resource] :

• 作者： Dey, Nilanjan, 1984- author.
• 其他作者：
  • Borah, Samarjeet, author.
  • Babo, Rosalina, 1967- author.
  • Ashour, Amira, 1975- author.
• 出版： London : Academic Press, an imprint of Elsevier
• 版本：First edition.
• 主題： Online social networks. , Social sciences--Network analysis. , Electronic books.
• ISBN： 9780128156414 (electronic bk.) 、 0128156414 (electronic bk.) 、 9780128154588 (pbk.) 、 0128154586 (pbk.)
• FIND@SFXID: CGU
• 資料類型: 電子書
• 內容註: Includes bibliographical references and index. Classification and analysis of Facebook metrics dataset using supervised classifiers -- An overview on social networking: design, issues, emerging trends, and security -- Emergence of stable and glassy states in dynamics of social networks -- De-anonymization techniques for social networks -- An analysis of demographic and behavior trends using social media: Facebook, Twitter, and Instagram -- Social network influence on mode choice and carpooling during special events: the case of Purdue game day -- Sentiment analysis on a set of movie reviews using deep learning techniques -- Sentiment analysis for airlines services based on twitter dataset -- Multilateral interactions and isolation in middlemen-driven network games -- The interplay of identity and social network: a methodological and empirical study -- Social networks and their uses in the field of secondary education -- NGOs' communication and youth engagement in the digital ecosystem.
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• 摘要註: 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.
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