Size A measure of the number of actors in a complete or egocentric network. Density The number of ties in the network reported as a fraction of the total possible number of ties. Reciprocity The proportion of mutual ties in a network. Distance The number of between any two actors in a network. “steps” Clustering coefficient A measure of a network’s actors’ tendency to together” into pockets of dense connectivity. In the first image, the blue node has a clustering coefficient of one, because all possible connections among its neighboring nodes have been realized. In the second image, only one of the possible connections has been realized so the — blue node has a clustering coefficient of 1/3. In the third image, none of the neighboring nodes are connected, so the blue node has a clustering coefficient of 0. Image via Wikipedia. Centrality There are multiple ways to determine a node’s importance, or centrality. The measure you use depends on how you define centrality. Several of these measures are: Degree centrality An important node is involved in large number of interactions. The number of edges connected with a particular node. Eigenvector centrality An important node is connected to important neighbors. This is a measure of influence of a given node in the whole network. The notion is how a given node is with other well connected nodes in the network. This is how, for example, Google determines page rank. Betweenness centrality An important node lies on a high proportion of between other nodes in the network. Model based on communication flow. A person who lies on communication paths can control communication flow, and is thus important. Closeness centrality
Size A measure of the number of actors in a complete or egocentric network. Density The number of ties in the network reported as a fraction of the total possible number of ties. Reciprocity The proportion of mutual ties in a network. Distance The number of “steps” between any two actors in a network. Clustering coefficient A measure of a network’s actors’ tendency to together” into pockets of dense connectivity. In the first image, the blue node has a clustering coefficient of one, because all possible connections among its neighboring nodes have been realized. In the second image, only one of the possible connections has been realized — so the blue node has a clustering coefficient of 1/3. In the third image, none of the neighboring nodes are connected, so the blue node has a clustering coefficient of 0. Image via Wikipedia. Centrality There are multiple ways to determine a node’s importance, or centrality. The measure you use depends on how you define centrality. Several of these measures are: Degree centrality An important node is involved in large number of interactions. The number of edges connected with a particular node. Eigenvector centrality An important node is connected to important neighbors. This is a measure of influence of a given node in the whole network. The notion is how a given node is with other well connected nodes in the network. This is how, for example, Google determines page rank. Betweenness centrality An important node lies on a high proportion of between other nodes in the network. Model based on communication flow. A person who lies on communication paths can control communication flow, and is thus important. Closeness centrality
Social-Network-Analysis-Glossary.docx