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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
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Overview of Social Network Analysis and Different Graph File Formats
Abhishek B.1* and Sumit Hirve2
1 Department of Mechanical Engineering, University of Applied Sciences, Emden Leer, Germany
2 Department of Computer Engineering, College of Engineering Pune, Pune, India
Abstract
Evaluating the public data from person-to-person communication destinations through the social network could create invigorating outcomes and bits of knowledge on the general assessment of practically any product, administration, or conduct. One of the best and precise public notion pointers is through information mining from social networks, as numerous clients seem to state their viewpoints on the social networks. The innovation in the Internet technologies figured out how to expand action in contributing to a blog, labeling, posting, and online informal communication. Therefore, individuals are beginning to develop keen on mining these immense information assets to evaluate the viewpoints. The Social Network Analysis (SNA) is the way toward researching social designs using graph hypothesis and networks. It integrates an assortment of procedures for analyzing the design of informal organizations, in addition with the hypotheses that target describing the hidden elements and the patterns in this framework. It is an intrinsically integrative field, which initially emerged from the sectors of graph hypothesis, statistics, and sociopsychology. This chapter will cover the hypothesis of SNA, with a short prologue to graph hypothesis and data spread. Then discuss the role of Python in SNA, followed up by building and suggesting informal communities from genuine Pandas and text-based data sets.
Keywords: Data mining, SNA, viewpoint dynamics, graph hypothesis, Python
1.1 Introduction—Social Network Analysis
A network of interactions, where the nodes comprise of number of people, and the edges comprise of interaction among the people are termed as social network [1]. The numbers of social networks and the strategies to analyze them are available since the past decades [2]. Statistics, graph theory, and sociology are the basics for the development of the area of social networks and are used in number of fields, such as business, economy, and information science [3, 4]. The analysis of a social network is analogous to the analysis of a graph because of the presence of graph, like topology of the social network. Graph analysis consists of a number of strategies but is not suitable to analyze the social networks [5–7] because of its complex characteristics. A very large-sized social network comprises of millions of edges and nodes, where the node generally possess number of attributes. The complex and large graph of social network cannot be managed