@inproceedings{5e599cebce994d70a6a2ceb5582326ac,
title = "Community detection using time-dependent personalized PageRank",
abstract = "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.",
author = "Haim Avron and Lior Horesh",
note = "Publisher Copyright: {\textcopyright} Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.; null ; Conference date: 06-07-2015 Through 11-07-2015",
year = "2015",
language = "אנגלית",
series = "32nd International Conference on Machine Learning, ICML 2015",
publisher = "International Machine Learning Society (IMLS)",
pages = "1795--1803",
editor = "Francis Bach and David Blei",
booktitle = "32nd International Conference on Machine Learning, ICML 2015",
}