  Exploratory factor analysis (EFA) is useful to identify the number and
mapping of the hidden factors that could dominantly represent the features in
the dataset. Principal component analysis (PCA) is the first step as part of
the two-step procedure to conduct EFA, with the number of dominant principal
components being the number of hidden factors and the entries for the features
in the corresponding Eigenvectors serve as the initial values of the factor
loadings. In this paper, we conduct EFA on a suite of 80 complex network
datasets to identify the number and mapping of the hidden factors (expected to
be less than four) that could dominantly represent the values incurred by the
vertices with respect to the four major centrality metrics (degree: DEG,
eigenvector: EVC, betweenness: BWC and closeness: CLC).
