Social network analysis (SNA)
Social Network Analysis (SNA) is a method for mapping and measuring the relationships and interactions among various entities, which can include individuals, groups, and even digital elements like computers or URLs. In SNA, entities are represented as nodes, while the connections between them are depicted as links, illustrating the flow of information, resources, or relationships. The analysis can be categorized as sociocentric, which focuses on entire networks, or egocentric, which examines personal connections and their implications, such as in the context of healthcare or social behavior.
SNA employs both mathematical and visual tools to uncover patterns that may influence social structures and individual behaviors, revealing how these links create opportunities or constraints within the network. The concept has roots in classical sociology and was formalized in the 20th century, gaining momentum with advancements in computing technologies. It is utilized across various fields, including anthropology, psychology, and business management, to understand organizational dynamics and social phenomena.
SNA not only provides insights into individual roles within a network—like connectors or isolates—but also scrutinizes the overall structure and efficiency of the network itself. This analysis can enhance our understanding of social interaction patterns and has practical applications in areas such as disease spread, organizational behavior, and even innovation, emphasizing the interconnectedness of all actors within a network.
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Subject Terms
Social network analysis (SNA)
Social network analysis (SNA) involves mapping and measuring relationships among people, groups, even computers and URLs—any source of information or knowledge that is connected to another or others. In this network the nodes are the people, computers, and groups—the hardware—while the links are flows or relationships. SNA of human relationships is both mathematical and visual. The use of SNA by business management consultants is called organizational network analysis. Network analysis is used in anthropology, ethnology, sociology, and psychology. SNA is applicable across disciplines to any problem involving social context. Sociocentric or society-focused SNA studies whole networks and the relations of all actors in the group. Examples include gangs, classrooms, boards of directors, or villages. Structure within the group is the focus of sociocentric SNA. Egocentric or personal networks are those people an individual knows. Egocentric studies are useful in research into such issues as the link between IV drug use and HIV transmission.
Background
Analysts assume that patterns are significant in the lives of players and that an individual life plays out differently depending on how it ties to a larger social network or web. Patterns of internal structure, some say, determine the success or failure of a society or organization.
The Bible emphasized descent lists, an indicator that societal organization mattered. In classical sociology the tendency was to emphasize cultural formation, physical elements, or the structure of the society. Theorists in Germany such as Ferdinand Tönnies and Georg Simmel began emphasizing face-to-face contact as it changed within the network, and in the first decade of the twentieth century their ideas spread to the sociological community in the United States. Modern analysis began in the 1930s, and in 1934 Jacob Moreno introduced sociometry, the quantitative measurement of social relationships. As World War II ended, Alex Bavelas founded MIT’s Group Networks Laboratory.
SNA uses mathematical terms and studies empirical data systematically. Only in the 1970s when computers became significantly more powerful and graph theory and other modern discrete combinatorics or counting methods became practical did SNA become a hot interdisciplinary field. Since the 1970s it has grown rapidly and become valuable in research on organizational behavior, relations between organizations, animal social organization, the spread of contagious disease, and other areas. SNA is international, and organizations, journals, textbooks, research centers, training centers, and computer programs are available to facilitate analysis. The professional organization for SNA researchers is the International Network for Social Network Analysis (INSNA), founded in 1977 by Barry Wellman. Its publication is called Connections, and it hosts the annual Sunbelt conference and provides electronic services. The INSNA website provides links to the Journal of Social Networks and maintains a database of practitioners.
Overview
SNA assumes that relationships are important, and the theory deals with relational concepts or processes. The practice assumes that actors and actions are not stand-alone but are interdependent. Relationships or links are paths for transfer or flow of material or nonmaterial resources. The structure of the network either offers constraints or opportunities for individuals. Social, economic, political, and other structures reveal long-term patterns of relationships. Network analysis studies not just individuals but the collective and its linkages. Linkages can be dyads, triads, subgroups, or networks. Social network theory has similarities to complexity and systems theory.
SNA attempts to pattern interactions among people. It allows the observer to discern the patterns that underlie seemingly random activity, the logic of movement, or the social arrangement of the grouping. By detaching to the level that people become the size of moving dots, an observer can see that the dots do not act randomly but instead have consistencies, with some dots normally together, others meeting often, others having no contact with one another—all indicators of patterns. The patterns are social networks, and the study of them is SNA.
SNA actors are commonly people but can also be animals, nations, or organizations. SNA maps flow and measure relationships. Nodes are the people or groups and the links show flows. SNA offers mathematical and visual analysis of complex systems, usually human.
SNA involves evaluation of the location of the players in the network. Location is an indicator of centrality, and differing degrees of centrality mean differing groupings and roles. Some types of roles include connectors, leaders, mavens, isolates, and bridges. SNA also explains where the clusters lie as well as who are members, who is core, and who is peripheral. Networks vary by degree of centralization, with high centralization meaning the lowest number of central nodes. With few nodes, loss of one can bring critical damage, even failure. Less centralized networks are more likely to survive the loss of a key or hub node.
Connection requires regular communication between nodes. Occasional or no communication means a player is unconnected. The degree of network activity and the number of direct connections to the node indicates network activity. The highest degree of activity is the connector or hub. It is not the number of connections but the number of important ones—the number of links to otherwise unconnected nodes. Nodes that serve to link otherwise unlinked nodes reflect a type of centrality known as betweenness. Closeness, another type of centrality, means proximity, and it offers the shortest paths to the others, the fastest connections—and it offers the best overview of network activity.
Another element is network reach. Nodal influence is not infinite. Shorter paths are more significant than longer ones, while key paths involve one or two steps, but not three or more because of the network horizon that limits the ability to see or influence. Measurement of networks is often by geodesics or shortest paths, but integration includes not just shortest but second-shortest paths and direct and indirect ties.
Network efficiency requires that a node be on the maximum number of efficient paths tied to both local and distant information. Rather than six degrees of separation, the reality is more often only three. Nodes need to know their neighborhood and neighbors within the network.
Nodes that have not just local but also more distant concurrent connections to overlapping groups are known as boundary spanners, because they bridge beyond the local to the broader network. Boundary spanners are more likely to be innovative because they are exposed to a broader array of ideas. Even peripheral nodes may be important if they are far enough on the periphery to tie to networks unavailable to more central nodes. In the business world, they can be vendors or contractors.
Bibliography
Alhajj, Reda, and Jon Rokne. Encyclopedia of Social Network Analysis and Mining. New York: Springer, 2014. Print.
Borgatti, Stephen P. and Martin G. Everett. Analyzing Social Networks. Thousand Oaks: Sage, 2013. Print.
Can, Fazli, Tansel Özyer, and Faruk Polat. State of the Art Applications of Social Network Analysis. New York: Springer, 2014. Print.
Carrington, Peter J., John Scott, and Stanley Wasserman, eds. Models and Methods in Social Network Analysis. Cambridge: Oxford UP, 2005. Print.
Gretzel, Ulrike. “Social Network Analysis: Introduction and Resources.” Web. 20 Dec. 2013.
Gündüz-Öğüdücü, Şule, and A. Şima Etaner-Uyar, eds. Social Networks: Analysis and Case Studies. New York: Springer, 2014. Print.
Kadushin, Charles. Understanding Social Netowrks: Theories, Concepts ,and Findings. New York: Oxford UP, 2012. Print.
Krebs, Valdis. Social Network Analysis, A Brief Introduction. Orgnet, 2013. Web. 20 Dec. 2013.
Prell, Christina. Social Network Analysis: History, Theory and Methodology. Thousand Oaks: Sage, 2011. Print.
Scott, John G. Social Network Analysis. 3rd ed. Thousand Oaks: Sage, 2012. Print.
“What Is INSNA?” INSNA. INSNA n. d. Web. 20 Dec. 2013.