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Social network analysis (SNA) is a process of quantitative and qualitative analysis of a social network. SNA measures and maps the flow of relationships and relationship changes between knowledge-possessing entities. Simple and complex entities include websites, computers, animals, humans, groups, organizations and nations.
The SNA structure is made up of node entities, such as humans, and ties, such as relationships. The advent of modern thought and computing facilitated a gradual evolution of the social networking concept in the form of highly complex, graph-based networks with many types of nodes and ties. These networks are the key to procedures and initiatives involving problem solving, administration and operations.
SNA usually refers to varied information and knowledge entities, but most actual studies focus on human (node) and relational (tie) analysis. The tie value is social capital.
SNA is often diagrammed with points (nodes) and lines (ties) to present the intricacies related to social networking. Professional researchers perform analysis using software and unique theories and methodologies.
SNA research is conducted in either of the following ways:
A snowball network forms when alters become egos and can create, or nominate, additional alters. Conducting snowball studies is difficult, due to logistical limitations. The abstract SNA concept is complicated further by studying hybrid networks, in which complete networks may create unlisted alters available for ego observation. Hybrid networks are analogous to employees affected by outside consultants, where data collection is not thoroughly defined.
Three analytical tendencies make SNA distinctive, as follows: