An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks - Freund, Alexander J.; Giabbanelli, Philippe J. (Frontiers in Big Data, 2022)
Node centrality measures are among the most commonly used analytical techniques for networks. They have long helped analysts to identify `important' nodes that hold power in a social context, where damages could have dire consequences for transportation applications, or who should be a focus for prevention in epidemiology. Given the ubiquity of network data, new measures have been proposed, occasionally motivated by emerging applications or by the ability to interpolate existing measures. Before analysts use these measures and interpret results, the fundamental question is: are these measures likely to complete within the time window allotted to the analysis? In this paper, we comprehensively examine how the time necessary to run 18 new measures (introduced from 2005 to 2020) scales as a function of the number of nodes in the network. Our focus is on giving analysts a simple and practical estimate for sparse networks. As the time consumption depends on the properties in the network, we nuance our analysis by considering whether the network is scale-free, small-world, or random. Our results identify that several metrics run in the order of O(n log n) and could scale to large networks, whereas others can require O(n^2) or O(n^3) and may become prime targets in future works for approximation algorithms or distributed implementations.