The Dataverse Network Project

Use these guidelines when subsetting or analyzing network data:

• For a Page rank network measure, the value for the parameter <d> is a proportion and must be between 0 and 1. Higher values of <d> increase dispersion, while values of <d> closer to zero produce a more uniform distribution. PageRank is normalized so that all of the PageRanks sum to 1.
• For a Bonacich Centrality network measure, the alpha parameter is a proportion that must be between -1 and +1. It is normalized so that all alpha centralities sum to 1.
• For a Bonacich Centrality network measure, the exo parameter must be greater than 0. A higher value of exo produces a more uniform distribution of centrality, while a lower value allows more variation.
• For a Bonacich Centrality network measure, the original alpha parameter of alpha centrality takes values only from -1/lambda to 1/lambda, where lambda is the largest eigenvalue of the adjacency matrix. In this Dataverse Network implementation, the alpha parameter is rescaled to be between -1 and 1 and represents the proportion of 1/lambda to be used in the calculation. Thus, entering alpha=1 sets alpha to be 1/lambda. Entering alpha=0.5 sets alpha to be 1/(2*lambda).