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W8 Centrality (Concept of Centrality (Measurement: computed to a single…
W8 Centrality
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Degree centrality
Actors who have more ties to other actors may be advantaged positions. the number of in-ties (in-degree) and out-ties (out-degree) of the modes suggests that certain actors are more “central” here
Freeman’s approach: they express the degree ofinequality or variance in our network as a percentage of that of a perfectstar network of the same size.
Bonacich approach: Bonacich argued that one's centrality is a function of how many connections one has, and how many the connections the actors in the neighborhood had.
Undirected
Degree centrality
Can be calculated without having information about the full network. And can interpret degree centrality in a variety of ways
Eigenvector centrality
A node with small degree have a higher score than a node with high degree if the first node's friends are very popular while the second nodes friends are not.
Beta centrality
both degree and eigenvector centrality is beta centrality. The advantage of beta Centrality is that we can choose in-between values of B that reflect our conception of how much longer channels of influence matter
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Closeness centrality
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emphasize the distance of an actor to allothers in the network by focusing on the distance from each actor to allothers
Reach distances : An index of the "reach distance" from each ego to (or from) all others is calculated .
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An inverse measurement of centrality
Large number-highly peripheral ; Small number-a node is more central.
The number of distinct nodes within K links of a given node
How many nodes a given node can reach in K or fewer steps.
A measure of how often a given node falls along the shortest path between two other nodes.
Nodes with high betweenness are in the position to threaten the network with disruption of operation.
Directed, non-valued networks
1.Degree:
Outdegree – The number of outgoing tie
Indegree – The number of incoming tie
2.Eigenvector (特征向量 )and beta centrality. It can be split into two concepts: right eigenvectors(outdegree); left eigenvector(indegree)
3.Closeness and K-reach centrality
Out K - reach centrality as the proportion of actors that a given actor can reach in k steps or less.
In K – reach centrality as the proportion of actors that can reach a given actor in k steps or less.