Graph topology recovery for regular and irregular graphs

Rohan Varma, Siheng Chen, Jelena Kovačević

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

Abstract

In this paper, we study the recovery of the graph topology or structure. We first extend our previous work on graph signal recovery to present a joint graph signal and structure recovery framework. By doing this, we allow the algorithm to learn a graph structure from noisy and incomplete graph signals and recover the graph signals at the same time. In this paper, we particularly focus on the specific subproblem of graph structure learning and develop algorithms towards this problem and analyze them. We briefly study the implications when the underlying true graph structure is irregular or regular. Finally, we validate the proposed methods for both synthetic data and the real-world recovery problem of semi-supervised digit-image classification.

Original languageEnglish (US)
Title of host publication2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
ISBN (Electronic)9781538612514
DOIs
StatePublished - Mar 9 2018
Event7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 - Curacao
Duration: Dec 10 2017Dec 13 2017

Publication series

Name2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
Volume2017-December

Conference

Conference7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Keywords

  • discrete signal processing on graphs
  • graph structure recovery
  • sampling
  • signal recovery

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

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  • Cite this

    Varma, R., Chen, S., & Kovačević, J. (2018). Graph topology recovery for regular and irregular graphs. In 2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017 (pp. 1-5). [8313202] (2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2017.8313202