Random Walks with Restarts for Graph-Based Classification: Teleportation Tuning and Sampling Design

Dimitris Berberidis, Athanasios N. Nikolakopoulos, Georgios B Giannakis

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations
305 Downloads (Pure)

Abstract

The present work introduces methods for sampling and inference for the purpose of semi-supervised classification over the nodes of a graph. The graph may be given or constructed using similarity measures among nodal features. Leveraging the graph for classification builds on the premise that relation among nodes can be modeled via stationary distributions of a certain class of random walks. The proposed classifier builds on existing scalable random-walk-based methods and improves accuracy and robustness by automatically adjusting a set of parameters to the graph and label distribution at hand. Furthermore, a sampling strategy tailored to random-walk-based classifiers is introduced. Numerical tests on benchmark synthetic and real labeled graphs demonstrate the performance of the proposed sampling and inference methods in terms of classification accuracy.

Original languageEnglish (US)
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2811-2815
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
StatePublished - Sep 10 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Calgary, Canada
Duration: Apr 15 2018Apr 20 2018

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2018-April
ISSN (Print)1520-6149

Other

Other2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018
CountryCanada
CityCalgary
Period4/15/184/20/18

Bibliographical note

Funding Information:
Work in this paper was supported by the NSF Grants No. 1500713, No. 1514056, and No. 1711471

Publisher Copyright:
© 2018 IEEE.

Keywords

  • PageRank
  • Random Walks
  • Sampling on Graphs
  • Seed Set Expansion
  • Semi-Supervised Learning

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