Efficient computation of Katz centrality for very dense networks via negative parameter Katz

TL;DR

This paper introduces an efficient method to compute Katz centrality in very dense networks by leveraging the complement graph and negative parameters, offering new insights and practical algorithms for dense network analysis.

Contribution

It presents a novel approach to compute Katz centrality efficiently in dense networks using complement graphs and negative parameters, extending applicability beyond sparse graphs.

Findings

Efficient computation of Katz centrality in dense networks via complement graphs.

Interpretation of negative parameter Katz as Katz on the complement graph.

Approximation method for weighted graphs with conditions for correct ranking.

Abstract

Katz centrality (and its limiting case, eigenvector centrality) is a frequently used tool to measure the importance of a node in a network, and to rank the nodes accordingly. One reason for its popularity is that Katz centrality can be computed very efficiently when the network is sparse, i.e., having only $O(n)$ edges between its $n$ nodes. While sparsity is common in practice, in some applications one faces the opposite situation of a very dense network, where only $O(n)$ potential edges are missing with respect to a complete graph. We explain why and how, even for very dense networks, it is possible to efficiently compute the ranking stemming from Katz centrality for unweighted graphs, possibly directed and possibly with loops, by working on the complement graph. Our approach also provides an interpretation, regardless of sparsity, of "Katz centrality with negative parameter" as usual Katz centrality on the complement graph. For weighted graphs, we provide instead an approximation method that is based on removing sufficiently many edges from the network (or from its complement), and we give sufficient conditions for this approximation to provide the correct ranking. We include numerical experiments to illustrate the advantages of the proposed approach.