Biased Selection for Building Small-World Networks

Abstract. Small-world networks are currently present in many distributed applications and can be built augmenting a base network with long-range links using a probability distribution. Currently available distributed algorithms to select these long-range neighbors are designed ad hoc for specific probability distributions. In this paper we propose a new algorithm called Biased Selection (BS) that, using a uniform sampling service (that could be implemented with, for instance, a gossip-based protocol), allows to select long-range neighbors with any arbitrary distribution in a distributed way. This algorithm is of iterative nature and has a parameter r that gives its number of iterations. We prove that the obtained sampling distribution converges to the desired distribution as r grows. Additionally, we obtain analytical bounds on the maximum relative error for a given value of this parameter r. Although the BS algorithm is proposed in this paper as a tool to sample nodes in a network, it...

• Type: Journal
• Year: 2010
• Where: OPODIS
• Authors: Andrés Sevilla, Alberto Mozo, M. Araceli Lorenzo, José Luis López-Presa, Pilar Manzano, Antonio Fernández Anta
• Keywords: Algorithm, Distributed And Parallel Computing, Long-range Neighbors, OPODIS 2010, Small-world Networks