Social network analysis

Social Network Analysis

A visualization of a social network on Facebook.

Social network analysis (SNA) is the process of investigating social structures through the use of networks and graph theory. It characterizes networked structures in terms of nodes (individual actors, people, or things within the network) and the ties, edges, or links (relationships or interactions) that connect them. Examples of social structures commonly visualized through SNA include social media networks, interpersonal relationships, and organizational structures.

History

The origins of social network analysis can be traced back to the early 20th century, with foundational work by sociologists such as Émile Durkheim and Georg Simmel. However, it was not until the 1930s and 1940s that the field began to take shape with the work of Jacob Moreno and Helen Jennings, who developed the sociogram, a graphical representation of social links.

Key Concepts

Nodes and Edges

In SNA, nodes represent the entities within the network, which can be individuals, groups, or organizations. Edges represent the relationships or interactions between these nodes. These can be directed or undirected, weighted or unweighted, depending on the nature of the relationship.

Centrality Measures

Centrality measures are used to identify the most important nodes within a network. Common centrality measures include:

• Degree Centrality: The number of direct connections a node has.
• Betweenness Centrality: A measure of the number of times a node acts as a bridge along the shortest path between two other nodes.
• Closeness Centrality: The average length of the shortest path from the node to all other nodes in the network.
• Eigenvector Centrality: A measure of the influence of a node in a network, based on the idea that connections to high-scoring nodes contribute more to the score of the node in question.

A graph illustrating betweenness centrality.

Network Density

Network density is a measure of the proportion of potential connections in a network that are actual connections. It provides insight into how interconnected the network is.

Clustering Coefficient

The clustering coefficient is a measure of the degree to which nodes in a graph tend to cluster together. It is used to determine the presence of tightly knit groups within the network.

Diagram showing various characteristics of social networks.

Applications

Social network analysis is used in a variety of fields, including sociology, anthropology, psychology, information science, and organizational studies. It is particularly useful in understanding the spread of information, the dynamics of social groups, and the structure of social networks.

Epidemiology

In epidemiology, SNA is used to model the spread of diseases through populations, helping to identify key individuals or groups that may be critical in controlling outbreaks.

Marketing

Marketers use SNA to identify influential individuals within social networks who can help spread information about products or services.

Organizational Studies

In organizations, SNA can reveal informal networks that influence decision-making and information flow, which may not be apparent from formal organizational charts.

An example of a network showing triplets and their connections.

Also see

• Graph theory
• Network theory
• Sociometry
• Complex networks
• Social media

References

• Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.
• Scott, J. (2017). Social Network Analysis. SAGE Publications.