Social Network Analysis Techniques

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ABSTRACT
Online social networks namely Facebook, Twitter and LinkedIn are the most popularly visited sites on the internet. These sites contain large voluminous data about the people and the relationships among them. Community structure is an important property of social networks. It is a topic of considerable interest in many areas due to its wide range of applications in multiple disciplines including biology, computer science, social science and so on. Detection of communities reveals how the structure of ties affects the peoples and their relationships. To facilitate community discovery a wide range of tools have been developed over years. This paper surveys several tools available for detection and mining of communities and presents a
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The field of social networks and their analysis has evolved from graph theory, statistics and sociology [1]. Social Network Analysis (SNA) is the study of social relations among a set of actors [18]. Social network analysis focuses on the analysis of patterns of relationships among people, organizations, positions and similar social entities. Social network analysis provides both a visual and a mathematical analysis of human relationships [17]. Since social networks form the topology of graph, analyzing a social network is similar to the analysis of a graph. There are a lots of social network analysis tools have been developed. Most of the tools are based on graph…show more content…
A community is a group of vertices which probably share common properties and/or play similar roles within the graph [10]. Communities are also called clusters or modules. Community detection in Online Social Networks (OSNs) is based on analyzing the structure of the network and finding individuals that correlate more with each other than with other users [20]. Clustering an individual in a particular way can help to further make an estimation regarding the individual such as the activities, products, and services, an individual might be interested in. Communities can be used for recommending information, understanding the structure of the given network and visualizing a large network [19]. Identifying high order structures within networks yields insights into their functional organization, which in turn contributes more knowledge while offering many possible actions, including marketing plans, recommendations and user interface

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