Social Network Analysis. Группа авторов
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Название: Social Network Analysis

Автор: Группа авторов

Издательство: John Wiley & Sons Limited

Жанр: Техническая литература

Серия:

isbn: 9781119836735

isbn:

СКАЧАТЬ 215

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       Scrivener Publishing

      100 Cummings Center, Suite 541J

      Beverly, MA 01915-6106

       Publishers at Scrivener

      Martin Scrivener ([email protected])

      Phillip Carmical ([email protected])

      Social Network Analysis

      Theory and Applications

      Edited by

      Mohammad Gouse Galety

       Chiai Al Atroshi

       Bunil Kumar Balabantaray

      and

       Sachi Nandan Mohanty

      This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA

      © 2022 Scrivener Publishing LLC

      For more information about Scrivener publications please visit www.scrivenerpublishing.com.

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       Library of Congress Cataloging-in-Publication Data

      ISBN 978-1-119-83623-0

      Cover image: Pixabay.Com Cover design by Russell Richardson

      Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines

      Printed in the USA

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      Preface

      By helping students envision the future, a teacher can help them prepare for it. On this transcendent note, we deigned this book to encourage students to take advantage of the possibilities and opportunities presented in the field of social networking. Several books have been written on the inexhaustible theme of Social Network Analysis over the last few decades. However, this book is a cumulative review of the new trends and applications manifested in areas of social networking.

      Our intention was to present an agglomeration of diverse themes of social networking analysis such as an introduction to Python for social networks analysis; handling real-world network datasets; the cascading behavioral pattern of social network users; social network structure and data analysis in healthcare; and a pragmatic analysis of the social web. Also presented are components of Semantic Web mining; classification of normal and anomalous activities in a network by cascading C4.5 decision tree and K-means clustering algorithms; a machine learning approach to forecast words in social media; a sentiment analysis-based extraction of real-time social media information from Twitter using natural language processing; and using cascading behavior in concepts and models to explore and analyze real-world social networking datasets.