Totally asynchronous distributed estimation of eigenvector centrality in digraphs with application to the PageRank problem
Paper in proceeding, 2016

We propose a distributed coordination mechanism which enables nodes in a directed graph to accurately estimate their eigenvector centrality (eigencentrality) even if they update their values at times determined by their own clocks. The clocks need neither be synchronized nor have the same speed. The main idea is to let nodes adjust the weights on outgoing links to compensate for their update speed: the higher the update frequency, the smaller the link weights. Our mechanism is used to develop a distributed algorithm for computing the PageRank vector, commonly used to assign importance to web pages and rank search results. Although several distributed approaches in the literature can deal with asynchronism, they cannot handle the different update speeds that occur when servers have heterogeneous computational capabilities. When existing algorithms are executed using heterogeneous update speeds, they compute incorrect PageRank values. The advantages of our algorithm over existing approaches are verified through illustrative examples.

asynchronous operation

eigencentrality estimation

PageRank problem

Distributed coordination


Themistoklis Charalambous

Chalmers, Signals and Systems, Communication, Antennas and Optical Networks

C. N. Hadjicostis

University of Illinois

University of Cyprus

M. G. Rabbat

McGill University

Mikael Johansson

Royal Institute of Technology (KTH)

55th IEEE Conference on Decision and Control, CDC 2016, Las Vegas, United States, 12-14 December 2016

0743-1546 (ISSN)

978-1-5090-1837-6 (ISBN)

Subject Categories

Electrical Engineering, Electronic Engineering, Information Engineering





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