Community detection using time-dependent personalized PageRank

Haim Avron, Lior Horesh

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Local graph diffusions have proven to be valuable tools for solving various graph clustering problems. As such, there has been much interest recently in efficient local algorithms for computing them. We present an efficient local algorithm for approximating a graph diffusion that generalizes both the celebrated personalized PageRank and its recent competitor/companion - the heat kernel. Our algorithm is based on writing the diffusion vector as the solution of an initial value problem, and then using a waveform relaxation approach to approximate the solution. Our experimental results suggest that it produces rankings that are distinct and competitive with the ones produced by high quality implementations of personalized PageRank and localized heat kernel, and that our algorithm is a useful addition to the toolset of localized graph diffusions.

Original languageEnglish
Title of host publication32nd International Conference on Machine Learning, ICML 2015
EditorsFrancis Bach, David Blei
Number of pages9
ISBN (Electronic)9781510810587
StatePublished - 2015
Externally publishedYes
Event32nd International Conference on Machine Learning, ICML 2015 - Lile, France
Duration: 6 Jul 201511 Jul 2015

Publication series

Name32nd International Conference on Machine Learning, ICML 2015


Conference32nd International Conference on Machine Learning, ICML 2015

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Computer Science Applications


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