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In the last decade, the study of social interaction has been formalized as social network analysis. Being a paradigm of social interactions based on graph theory, social network analysis has developed tools to formally represent and model social structure. Social Network Analysis has its origin in social sciences and merged itself into broader fields of Network Analysis and Graph Theory (Thapa et al, 2016; Torabi et al, 2016; Akhtar et al, 2014). Network Analysis deal with the problems which have a network structure and therefore can be represented in the form of a graph. Graph Theory gives a set of abstract methods for the analysis of problems depicted as a graph. There is a suitable combination of visualization and analytical tools developed for analysis of social networks. Social network analysis Social Network Analysis allows researchers to identify, map and analyze structural positions. It has the ability to highlight the components and links of social groups. It is a methodology to understand how the society functions (Ruan et al, 2016). Rather than focusing on individuals and their characteristics, it studies the relation between various entities and their properties as a group. It emphasizes mainly on the relations between various entities, which are defined as linkages among nodes.
The smallest social network is composed of only two actors and is called a dyad. A subset of three actors is called a triad. Actors between which ties can be made from a group and can be usually represented with a graph which is called a sociogram. A network is a set of nodes connected by a set of ties. These nodes can be anything, persons, individuals, teams, organizations, concepts, patents etc. In case of social networks, the nodes are individuals. The networks can be homogenous, which consists of similar nodes and heterogeneous, which are made up of dissimilar nodes. The ties can be directed, undirected, dichotomous or weighted (Fan et al, 2014). When focus is on a single node, that node is termed as ego and the nodes it has ties with are called alters. The data collected by network analysis is relational data, which is represented in matrix form or graphic form. The ways to represent network is graph, edge list and adjacency matrix.
The basic concepts of social network analysis are network, tie strength, node strength and cohesion. Tie strength helps to identify whether the relation between the entities is strong or weak. It is denoted by the weight of the tie (Augustyniak et al, 2014). Weights could be frequency of interaction in a time period, number of items exchanged, amount of information flow, the distance between source and destination or a combination of a few factors. Edge weights signify the relationship strength in social networks.
Other attributes of social networks are homophily, transitivity and bridging. Homophily is the tendency to relate with people of similar characteristics. This leads to formation of of clusters where formation of ties is easier. The drawback of this feature is that it does not promote any innovation or interaction between entities of diverse ideas. Transitivity is the property of ties which suggests that if there is a tie between A and B, another tie between B and C, then in a transitive network, A and C will also be connected. Bridges are nodes and edges that connect across groups. These nodes facilitate inter group communication and promote exchange of information among less connected groups (Yang et al, 2014; Silva et al, 2015; Munger et al, 2015; Rossetti et al, 2015).