Simulating Network Influence Algorithms Using Particle-Swarms: PageRank and PageRank-Priors

A particle-swarm is a set of indivisible processing elements that traverse a network in order to perform a distributed function. This paper will describe a particular implementation of a particle-swarm that can simulate the behavior of the popular PageRank algorithm in both its global-rank and relative-rank incarnations. PageRank is compared against the particleswarm method on artificially generated scale-free networks of 1,000 nodes constructed using a common gamma value, γ = 2.5. The running time of the particle-swarm algorithm is O(|P | + |P |t) where |P | is the size of the particle population and t is the number of particle propagation iterations. The particle-swarm method is shown to be beneficial in its ease of extension and running time.