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Network analysis is a means of investigating the connections and relationships between a number of entities. While originally based in mathematical graph theory, the application of network analysis methods has become common across academic disciplines, from English to Biology. Whether mapping the relationships between characters in a novel, to charting protein structures, the modern practice of network analysis is supported by a number of digital tools.
Commonly used terms in network analysis include:
Node: the elements of a network that are connected. Also called an actor or a vector.
Edge: a connection, or link, between nodes.
Centrality: a numerical expression of a node's importance, which can be measured in four primary ways:
Degree Centrality: the number of edges connected to a node.
Closeness Centrality: the closeness of the entire network.
Betweenness Centrality: the degree to which a node connects other nodes together.
Eigenvector Centrality: connection to other, well-connected nodes.
One of the foundational works on sociological network theory, which argues for the counter-intuitive importance of the weaker ties in a social network in maintaining the overall network's cohesion and structure.