measures_in_social_network_analysis

- Betweenness
- The extent to which a node lies between other nodes in the network. This measure takes into account the connectivity of the node's neighbors, giving a higher value for nodes which bridge clusters. The measure reflects the number of people who a person is connecting indirectly through their direct links.

- Bridge
- An edge is said to be a bridge if deleting it would cause its endpoints to lie in different components of a graph.

- Centrality
- This measure gives a rough indication of the social power of a node based on how well they “connect” the network. “Betweenness”, “Closeness”, and “Degree” are all measures of centrality.

- Centralization
- The difference between the number of links for each node divided by maximum possible sum of differences. A centralized network will have many of its links dispersed around one or a few nodes, while a decentralized network is one in which there is little variation between the number of links each node possesses.

- Closeness
- The degree an individual is near all other individuals in a network (directly or indirectly). It reflects the ability to access information through the “grapevine” of network members. Thus, closeness is the inverse of the sum of the shortest distances between each individual and every other person in the network. (See also: Proxemics) The shortest path may also be known as the “geodesic distance”.

- Clustering coefficient
- A measure of the likelihood that two associates of a node are associates themselves. A higher clustering coefficient indicates a greater 'cliquishness'.

- Cohesion
- The degree to which actors are connected directly to each other by cohesive bonds. Groups are identified as ‘cliques’ if every individual is directly tied to every other individual, ‘social circles’ if there is less stringency of direct contact, which is imprecise, or as structurally cohesive blocks if precision is wanted.

- Degree
- The count of the number of ties to other actors in the network. See also degree (graph theory).

- (Individual-level) Density
- The degree a respondent's ties know one another/ proportion of ties among an individual's nominees. Network or global-level density is the proportion of ties in a network relative to the total number possible (sparse versus dense networks).

- Flow betweenness centrality
- The degree that a node contributes to sum of maximum flow between all pairs of nodes (not that node).

- Eigenvector centrality
- A measure of the importance of a node in a network. It assigns relative scores to all nodes in the network based on the principle that connections to nodes having a high score contribute more to the score of the node in question.

- Local bridge
- An edge is a local bridge if its endpoints share no common neighbors. Unlike a bridge, a local bridge is contained in a cycle.

- Path length
- The distances between pairs of nodes in the network. Average path-length is the average of these distances between all pairs of nodes.

- Prestige
- In a directed graph prestige is the term used to describe a node's centrality. “Degree Prestige”, “Proximity Prestige”, and “Status Prestige” are all measures of Prestige. See also degree (graph theory).

- Radiality
- Degree an individual’s network reaches out into the network and provides novel information and influence.

- Reach
- The degree any member of a network can reach other members of the network.

- Structural cohesion
- The minimum number of members who, if removed from a group, would disconnect the group.

- Structural equivalence
- Refers to the extent to which nodes have a common set of linkages to other nodes in the system. The nodes don’t need to have any ties to each other to be structurally equivalent.

- Structural hole
- Static holes that can be strategically filled by connecting one or more links to link together other points. Linked to ideas of social capital: if you link to two people who are not linked you can control their communication.

measures_in_social_network_analysis.txt · Last modified: 2019/11/12 15:52 by hkimscil