Spreading in scale-free computer networks with improved clustering

Abstract

In this study, we investigated data spreading in computer networks with scale-free topology under various levels of improved clustering. Starting from a pure Barabási–Albert (BA) network topology, we applied a Poisson-based rewiring procedure with increasing rewiring probability, which promotes local connections. We then performed wired computer network simulations in NS2 simulator for these topologies. We found that for pure BA network, data transfer (throughput) is maximum, where time required for establishing routing scheme, end-to-end delays in data transmission and number of nodes acting in data transfer are at their minimum levels. Improving clustering increases these parameters those are at their minima. A noteworthy finding of this study is that, for moderate levels of clustering, total throughput remains close to its maximum yielding stable transfer rates, although number of infected nodes and end-to-end delay increase. This indicates that clustering promotes spreading phenomena in networks, although it increases average separation. As a result, clustering property emerges as a catalyzer in data spreading with minimal effects on the total amount of transmission.