Social Network Analysis

Social Network Analysis

Social Network Analysis | Network Encyclopedia

What is Social Network Analysis?

Social Network Analysis (SNA) is a methodological approach used to understand the structures and patterns of relationships within a network. It examines how nodes (individual actors, people, or things) and edges (the relationships or interactions between them) create the network's structure. SNA provides both a visual and a mathematical analysis of human relationships, offering insights into the dynamics of complex systems. This analytical tool is pivotal in various fields, including sociology, anthropology, business, public health, and information technology, to analyze social connections and their impacts on individual behaviors, organizational efficiency, and collective outcomes.

History of Social Network Analysis

The roots of Social Network Analysis can be traced back to early sociometry and graph theory. It began to take shape as a distinct field in the 1930s through the work of Jacob Moreno, who introduced sociograms to visualize interpersonal relationships within groups. Over the decades, SNA has evolved, incorporating advances in computer science, statistics, and theories from social sciences, leading to the development of sophisticated models and software for network analysis. 

Core Concepts of Social Network Analysis

  • Nodes: In SNA, nodes represent the individual entities within the network, such as people, organizations, or computers.
  • Edges: Edges are the connections between nodes, signifying relationships or interactions, which can be directed (one-way) or undirected (two-way).
  • Centrality Measures: These are metrics that identify the most important or influential nodes within a network. Key centrality measures include degree centrality, closeness centrality, betweenness centrality, and eigenvector centrality.
  • Clusters and Communities: SNA identifies clusters or communities within networks, where nodes are more densely connected to each other than to those outside the group.
  • Network Topology: This refers to the overall structure of the network, including its density, connectivity, and the presence of sub-networks or cliques.


  • Public Health: SNA is used to track the spread of diseases within communities, understand health-related behaviors, and identify key individuals who can influence healthy practices.
  • Business and Management: Organizations use SNA to improve communication, enhance collaboration, analyze organizational structures, and identify key influencers or bottlenecks in workflow processes.
  • Social Media Analysis: SNA techniques analyze online social networks to understand user behavior, viral content dissemination, and the dynamics of online communities.
  • Counterterrorism: Security agencies employ SNA to dissect the structures of terrorist networks, identifying key players and understanding communication patterns.


Social Network Analysis is a powerful tool that offers unique insights into the complex web of relationships that shape individual actions and collective outcomes. By mapping and measuring these connections, SNA helps researchers, policymakers, and organizations make informed decisions, understand community dynamics, and foster collaboration across various sectors. As networks continue to play a crucial role in societal functions, SNA remains an essential approach in the analysis and understanding of social structures.

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