**Centrality**is a structural measure of a network that gives an indication of relative importance of a node in the graph / network.

Simplest way of measuring centrality is by counting the number of connections a node has. This is called

**‘degree centrality’**.

Another way of measuring centrality is to see how far a node from all other nodes of the graph is is. This measure is called as **‘closeness centrality’** as it measures the path length between pairs of nodes.

**‘Betweenness Centrality’** is the measure of number of times the node acting as a bridge on the shortest path of any other two nodes. That gives how important each n ode in connecting the whole network.

To complicate the centrality further, we have a measure called **‘eigenvector centrality’**. Eigenvector considers the influence for the node in the network. This methods considers the power of the nodes the current node is connected. To explain it simply, if I am connected to 500 other people on LinkedIn is different from Barak Obama connecting to 500 of his friends on the LinkedIn. His 500 connections are more influential (*probably*) than my 500 connections. Google’s page rank is a variant of Eigenvector Centrality.

When an external factor is considered for each node and implement eigenvector centrality to consider an external α it is called **‘alpha centrality’**

When we move the alpha centrality measure from one node to cover multiple radii to include first degree, second degree and so on.. With a factors of β(i) and measure the centrality as a function of influence of varying degrees, it is called **beta centrality**.

*But in some situations power is not directly proportional to centrality. Think about it.*
## Leave a Reply