Asynchronous Distributed Power Iteration with Gossip-Based Normalization

  • Márk Jelasity
  • Geoffrey Canright
  • Kenth Engø-Monsen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4641)


The dominant eigenvector of matrices defined by weighted links in overlay networks plays an important role in many peer-to-peer applications. Examples include trust management, importance ranking to support search, and virtual coordinate systems to facilitate managing network proximity. Robust and efficient asynchronous distributed algorithms are known only for the case when the dominant eigenvalue is exactly one. We present a fully distributed algorithm for a more general case: non-negative square matrices that have an arbitrary dominant eigenvalue. The basic idea is that we apply a gossip-based aggregation protocol coupled with an asynchronous iteration algorithm, where the gossip component controls the iteration component. The norm of the resulting vector is an unknown finite constant by default; however, it can optionally be set to any desired constant using a third gossip control component. Through extensive simulation results on artificially generated overlay networks and real web traces we demonstrate the correctness, the performance and the fault tolerance of the protocol.


Overlay Network Vector Element Dominant Eigenvalue Failure Scenario PageRank Algorithm 
These 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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Márk Jelasity
    • 1
  • Geoffrey Canright
    • 2
  • Kenth Engø-Monsen
    • 2
  1. 1.University of Szeged, HAS Research Group on AIHungary
  2. 2.Telenor R&D, FornebuNorway

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