Community Detection Using Time-Dependent Personalized PageRank

Haim Avron, Lior Horesh
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:1795-1803, 2015.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-avron15, title = {Community Detection Using Time-Dependent Personalized PageRank}, author = {Avron, Haim and Horesh, Lior}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {1795--1803}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/avron15.pdf}, url = {https://proceedings.mlr.press/v37/avron15.html}, 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.} }
Endnote
%0 Conference Paper %T Community Detection Using Time-Dependent Personalized PageRank %A Haim Avron %A Lior Horesh %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-avron15 %I PMLR %P 1795--1803 %U https://proceedings.mlr.press/v37/avron15.html %V 37 %X 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.
RIS
TY - CPAPER TI - Community Detection Using Time-Dependent Personalized PageRank AU - Haim Avron AU - Lior Horesh BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-avron15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 1795 EP - 1803 L1 - http://proceedings.mlr.press/v37/avron15.pdf UR - https://proceedings.mlr.press/v37/avron15.html AB - 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. ER -
APA
Avron, H. & Horesh, L.. (2015). Community Detection Using Time-Dependent Personalized PageRank. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:1795-1803 Available from https://proceedings.mlr.press/v37/avron15.html.

Related Material