Core-Periphery Analysis Using Principal Components of the Neighborhood-based Bridge Node Centrality Tuple

The neighborhood-based bridge node centrality (NBNC) tuple has been proposed in the literature to rank nodes for the extent they could serve as a bridge node. The NBNC tuple of a node v has three entries- (# components in NGv, 1-algebraic connectivity ratio of NGv and degree of node v), where NGv is the neighborhood graph of node v. The research presented in this paper conducts principal component analysis on dataset comprising of NBNC tuples of all the nodes and computes a weighted PC_NBNC score based on the entries for the nodes in the dominating principal components (variances ≥ 1.0). The proposed model is to classify nodes as core (or peripheral) if their weighted PC_NBNC score is ≥ 0.0 (or < 0). The study measures the fractions of core-core, core-peripheral and peripheral-peripheral links and the fractions of core and peripheral nodes and uses these measures to classify a real-world network as either core-heavy or peripheral-heavy. Accordingly, 48 of the 80 real-world networks are classified as core-heavy (observed to be dominated by core nodes and core-core links) and the remaining 32 networks are classified as peripheral-heavy (observed to be dominated by peripheral nodes).