Distributed Uniform Sampling in Unstructured Peer-to-Peer Networks

— Uniform sampling in networks is at the core of a wide variety of randomized algorithms. Random sampling can be performed by modeling the system as an undirected graph with associated transition probabilities and defining a corresponding Markov chain (MC). A random walk of prescribed minimum length, performed on this graph, yields a stationary distribution, and the corresponding random sample. This sample, however, is not uniform when network nodes have a non-uniform degree distribution. This poses a significant practical challenge since typical large scale real-world unstructured networks tend to have non-uniform degree distributions, e.g., power-law degree distribution in unstructured peer-to-peer networks. In this paper, we present a distributed algorithm that enables efficient uniform sampling in large unstructured non-uniform networks. Specifically, we prescribe necessary conditions for uniform sampling in such networks and present distributed algorithms that satisfy these ...

• Type: Conference
• Year: 2006
• Where: HICSS
• Authors: Asad Awan, Ronaldo A. Ferreira, Suresh Jagannathan, Ananth Grama
• Keywords: Biometrics, HICSS 2006, Non-uniform Degree Distributions, System Sciences, Uniform Sampling, Unstructured Non-uniform Networks