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Scrivener Publishing
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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
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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
10 9 8 7 6 5 4 3 2 1
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.
We were delighted to see that many authors traversing many realms chose to contribute to this book. The topics covered are categorized according to themes. Chapter 1 discusses the hypothesis of social network analysis (SNA), with a short prologue to graph hypothesis and data spread. It projects the role of Python in SNA, followed up by building and suggesting informal communities
