Social Network Analysis

Social Network Analysis

Sheik Abdullah A. (Thiagarajar College of Engineering, India) and Abiramie Shree T. G. R. (Thiagarajar College of Engineering, India)
Copyright: © 2020 |Pages: 11
DOI: 10.4018/978-1-5225-9750-6.ch006


Each day, 2.5 quintillion bytes of data are generated due to our daily activity. It is due to the vast amount of use of the smart mobiles, Cloud data storage, and the Internet of Things. In earlier days, these technologies were utilized by large IT companies and the private sector, but now each person has a high-end smartphone along with the cloud and IoT for the easy storage of data and backup. The analysis of the data generated by social media is a tedious process and involves a lot of techniques. Some tools for social network analysis are: Gephi, Networkx, IGraph, Pajek, Node XL, and cytoscope. Apart from these tools there are various efficient social data analysis algorithms that are far more helpful in doing analytics. The need for and use of social network analysis is very helpful in our current problem of huge data generation. In this chapter, the need for the analysis of social data along with the tools that are needed for the analysis and the techniques that are to be implemented in the field of social data analysis are covered.
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Social Network Analysis

Social Network Analysis (SNA) is an analysis method in which the analysis process is done on the socially available data (Bifet & Frank, 2010). Here, the analysis method consists of nodes and edges of a graph. The node (Vertices V) which represents an individual user or customer or employee or company which varies depends on the data and the edges (E) which represents the relationship or association or intimacy of friendship between them. This type of analysis behavior is also known as sociograms or graph analysis or even graphical structure analysis of social data.

This type of analysis is used for the finding of the network structure of the social data which highlights the association of the social data between them. Community detection is one of the major uses of sociograms. In this method, the sociogram helps to identity how these people are connected to one another and can be helpful to justify them. This community detection method is widely used in the social media such as Twitter and Facebook. These types of representation are effective only if the social data is presented for a small group or for small analysis whereas for a large data analysis the use of the matrix representation of data is more useful. It represents the data in a matrix format where the columns and rows of the data are in vector format. Mostly all type of analysis on the social data helps to extract the relationship between the data and predict the association and justifies that cause of the connection between them.

Figure 1.

Sociogram representation data


Types Of Social Network Analysis

There are basically two methods for the analysis of the social data. They are, the researchers stated that Ego network analysis is associated with the analysis is done only on the nodes of the network (Akhtar, 2014). The possible answers from the nodes such as why they connected together? What the association of connection between them? These are the common findings from this type analysis and help to find the individuals persons network in the social site or their way of connection and communication between the people.

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