Guided Search and Distribution of Information Flow on Complex Graphs

Abstract

AbstractWithin the numerical model proposed in [1] we study the flow of information on a graph with power-law organization of links and an emergent superstructure formed by two highly interconnected hubs. The local search algorithms which navigate transport at each node can use information in the range of zero, one, and two-nearest neighbourhood of the node. We show how the flow carried by a particular link is distributed over graph when range of guided search and posting rate of packets are varied. The probability density function of the flow is given by a universal log-normal law, which is determined by the overall packet density in the stationary traffic. However, the distribution becomes unstable when the traffic experiences temporary or permanent jamming.KeywordsSearch AlgorithmLocal Search AlgorithmGiant ComponentComplex GraphTopological CommunityThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.