Home » Eigenvector centrality (Google Juice)

Eigenvector centrality (Google Juice)

Hue (from red=0 to blue=max) shows the node Eigenvector centrality.

Eigenvector centrality is 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 high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.

Google’s PageRank is a variant of the Eigenvector centrality measure as it sees how many are linking to you, how trusted those sites are (government or a long standing domain), and how large the traffic and network that those sites encounter.

The better the information ou put out, the more that people will link to it and then so and so on. This will help you increase our social capital by utilizing your network of trust. LinkedIn also works this way. If you provide reciprocity by recommending someone, they might be more apt to help you by introducing you to another person in your networks connection of trusted connections.

For the math fans out there:

Eigenvector centrality is one method of computing the “centrality”, or approximate importance, of each node in a graph. The assumption is that each node’s centrality is the sum of the centrality values of the nodes that it is connected to. The nodes are drawn with a radius proportional to their centrality. The adjacency matrix and centrality matrix for the solution are shown. The centrality matrix is an eigenvector of the adjacency matrix such that all of its elements are positive.