Linked from

The 24 pages that link to Social network analysis, each with the reason it gives.

• Graph theoryRelated: Graph measures reveal patterns such as centrality and community in social ties.

• SociologyRelated: It traces how ties shape access, influence, and collective behavior.

• Digital humanitiesRelated: It models connections among people, texts, institutions, or ideas in historical evidence.

• Vertex (graph theory)Related: Vertices can represent people or organizations, while edges encode their relationships.

• Breadth-first searchRelated: Breadth-first distances model degrees of separation in unweighted social networks.

• Methodological individualismRelated: Network patterns can be explained through individuals' ties and resulting interactions.

• Connected componentRelated: Components identify groups with no network path to the rest of the social graph.

• Power imbalanceRelated: Network position can reveal influence that formal titles do not show.

• Random graphRelated: Random graph null models help test whether social ties show nonrandom structure.

• Social media activismRelated: It helps explain how information and mobilization move through activists’ online connections.

• Dominance hierarchyRelated: Network methods represent dominance interactions and reveal patterns not captured by a single rank list.

• Weighted graphRelated: Weighted ties can represent interaction frequency, trust, or relationship strength.

• Adjacency listRelated: A person's connections can be stored as that vertex's neighbors.

• Transitive closureRelated: Closure distinguishes direct ties from connections reachable through intermediaries.

• Power eliteRelated: It can reveal ties linking leaders across otherwise separate institutions.

• Sociological theoryRelated: It turns relational theories into analyses of measurable patterns of connection.

• CliodynamicsRelated: Network methods help model historical ties among people, groups, and institutions.