Social Network Analysis

SOCIAL NETWORK ANALYSIS As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.

Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.

The book features:

• Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network.
• An understanding of network analysis and motivations to model phenomena as networks.
• Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted.
• Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption.
• Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems.
• The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research.
• The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers. Audience

The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.

Mohammad Gouse Galety, PhD, is an assistant professor in the Information Technology Department, Catholic University in Erbil, Erbil, Iraq.

Chiai Al-Atroshi is a lecturer in the Educational Counseling and Psychology Department, University of Duhok, Duhok, Iraq.

Bunil Kumar Balabantaray, PhD, is an assistant professor in the Department of Computer Science and Engineering, National Institute of Technology Meghalaya, India.

Sachi Nandan Mohanty, PhD, is an associate professor in the Department of Computer Science & Engineering at Vardhaman College of Engineering (Autonomous), Hyderabad, India.

SOCIAL NETWORK ANALYSIS

As social media dominates our lives in increasing intensity, the need for developers to understand the theory and applications is ongoing as well. This book serves that purpose.

Social network analysis is the solicitation of network science on social networks, and social occurrences are denoted and premeditated by data on coinciding pairs as the entities of opinion.

The book features:

• Social network analysis from a computational perspective using python to show the significance of fundamental facets of network theory and the various metrics used to measure the social network.

• An understanding of network analysis and motivations to model phenomena as networks.

• Real-world networks established with human-related data frequently display social properties, i.e., patterns in the graph from which human behavioral patterns can be analyzed and extracted.

• Exemplifies information cascades that spread through an underlying social network to achieve widespread adoption.

• Network analysis that offers an appreciation method to health systems and services to illustrate, diagnose, and analyze networks in health systems.

• The social web has developed a significant social and interactive data source that pays exceptional attention to social science and humanities research.

• The benefits of artificial intelligence enable social media platforms to meet an increasing number of users and yield the biggest marketplace, thus helping social networking analysis distribute better customer understanding and aiding marketers to target the right customers.

Audience

The book will interest computer scientists, AI researchers, IT and software engineers, mathematicians.

Auteur

Mohammad Gouse Galety, PhD, is an assistant professor in the Information Technology Department, Catholic University in Erbil, Erbil, Iraq.

Chiai Al-Atroshi is a lecturer in the Educational Counseling and Psychology Department, University of Duhok, Duhok, Iraq.

Bunil Kumar Balabantaray, PhD, is an assistant professor in the Department of Computer Science and Engineering, National Institute of Technology Meghalaya, India.

Sachi Nandan Mohanty, PhD, is an associate professor in the Department of Computer Science & Engineering at Vardhaman College of Engineering (Autonomous), Hyderabad, India.

Échantillon de lecture
1
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 using the old graph analysis strategies [8].

Email network, collaboration network, and telephone network are the various types of social networks. However, recent online social networks, like Twitter, Facebook, and LinkedIn, have gained increased popularity within a short period with a greater number of users. It was found with a survey that Facebook has …