Estimating accurately important nodes for routing in modern and future networks is a key process with numerous benefits. Towards this goal, in this paper we propose Hyperbolic Traffic Load Centrality (HTLC), as a novel alternative to the Traffic Load Centrality (TLC) metric, used for ranking nodes with respect to their importance in the routing operation. HTLC is based on network embedding in hyperbolic space, while assuming paths paved by greedy routing over hyperbolic coordinates, which requires less computational effort than shortest path routing (in terms of hop distances) used for TLC. Greedy routing in hyperbolic space also yields paths with lengths very close to the shortest ones for the social networks of interest bearing the scale-free property. Through analysis and simulation, we demonstrate that HTLC requires significantly lower computational time than TLC, and despite being more suitable for greedy routing constraints over hyperbolic space, it nevertheless achieves a close approximation of TLC for networks with scale-free properties when assuming shortest path routing. Thus, it can substitute TLC when analyzing very large network topologies.
['Eleni Stai', 'Konstantinos Sotiropoulos', 'Vasileios Karyotis', 'Symeon Papavassiliou']
Hyperbolic Traffic Load Centrality for large-scale complex communications networks