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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.

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 from genuine pandas and text-based datasets. Chapter 2 accords with graph representation, Network-X, scope of Python in SNA, and the installation and working environment of Python. Chapter 3 presents the basic principles of scale-free network and its primary scenarios for modeling and analyzing the performance of the network to provide an approximate data from a massive network such as social media. Chapter 4 deliberates the cascading behavioral pattern of social network users with the user-generated content consisting of images, text and videos. Machine learning algorithms and natural language processing help to understand the text content of data and the user behavioral pattern in social media. Chapter 5 develops a deep insight into SNA and its applications in the healthcare system.

Continuing on, Chapter 6 proposes an integrated model approach with social semantic ontology under a specific (agricultural) domain which is composed of domain ontology and social ontology. This integrated approach is used for establishing social semantic ontology. Chapter 7 elaborates the method of identification of anomalies with “K-means + C4.5,” the method of cascading K-means clustering and the C4.5 decision-tree methods for classifying anomalous and typical computer network operations. Chapter 8 establishes forecasting as one of the machine learning and supervised learning algorithms. It builds models that capture or explain the data to figure out the reason for the fundamental causes of a time series through a term frequency and inverse document frequency algorithm. Chapter 9 presents a machine learning algorithm using Naïve Bayes method that analyzes polarity in twitter streams. Sentiment analysis is effective in mining sentences taken from Twitter. Chapter 10 deciphers cascading behavior, and discusses its purpose and significance with special focus on decision-based, probabilistic, independent cascade, linear threshold and SIR models. The concept of centrality, cascading failure and cascading capacity are also elucidated. Chapter 11 devises a Python framework for analyzing the structural dynamics and functions of complex networks.

We sincerely believe that this book will prove to be a useful augmentation to Social Network Analysis. We would like to express our appreciation to the authors, publisher and the team members for their strenuous efforts. Lastly, we thank our family members for their support, encouragement and patience during the entire period of this work.

Dr. Mohammad Gouse Galety Mr. Chiai Al-AtroshiDr. Bunil Kumar Balabantaray Dr. Sachi Nandan Mohanty March 2022