Graph topology recovery for regular and irregular graphs

Rohan Varma, Siheng Chen, Jelena Kovacevic

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
Volume2017-December
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

Other

Other7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017
CityCuracao
Period12/10/1712/13/17

Fingerprint

Irregular
topology
Recovery
recovery
Topology
Graph in graph theory
image classification
digits
Image classification
learning
Structure Learning
Image Classification
Synthetic Data
Digit

Keywords

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

ASJC Scopus subject areas

  • Signal Processing
  • Control and Optimization
  • Instrumentation

Cite this

Varma, R., Chen, S., & Kovacevic, 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 (Vol. 2017-December, pp. 1-5). [8313202] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CAMSAP.2017.8313202

Graph topology recovery for regular and irregular graphs. / Varma, Rohan; Chen, Siheng; Kovacevic, Jelena.

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., 2018. p. 1-5 8313202.

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

Varma, R, Chen, S & Kovacevic, 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. vol. 2017-December, 8313202, Institute of Electrical and Electronics Engineers Inc., pp. 1-5, 7th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017, Curacao, 12/10/17. https://doi.org/10.1109/CAMSAP.2017.8313202
Varma R, Chen S, Kovacevic J. Graph topology recovery for regular and irregular graphs. In 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. 2018. p. 1-5. 8313202 https://doi.org/10.1109/CAMSAP.2017.8313202
Varma, Rohan ; Chen, Siheng ; Kovacevic, Jelena. / Graph topology recovery for regular and irregular graphs. 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., 2018. pp. 1-5
@inproceedings{95b467cc8bcb4adb820463a8ef3ab9a9,
title = "Graph topology recovery for regular and irregular graphs",
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.",
keywords = "discrete signal processing on graphs, graph structure recovery, sampling, signal recovery",
author = "Rohan Varma and Siheng Chen and Jelena Kovacevic",
year = "2018",
month = "3",
day = "9",
doi = "10.1109/CAMSAP.2017.8313202",
language = "English (US)",
volume = "2017-December",
pages = "1--5",
booktitle = "2017 IEEE 7th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Graph topology recovery for regular and irregular graphs

AU - Varma, Rohan

AU - Chen, Siheng

AU - Kovacevic, Jelena

PY - 2018/3/9

Y1 - 2018/3/9

N2 - 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.

AB - 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.

KW - discrete signal processing on graphs

KW - graph structure recovery

KW - sampling

KW - signal recovery

UR - http://www.scopus.com/inward/record.url?scp=85051118561&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85051118561&partnerID=8YFLogxK

U2 - 10.1109/CAMSAP.2017.8313202

DO - 10.1109/CAMSAP.2017.8313202

M3 - Conference contribution

AN - SCOPUS:85051118561

VL - 2017-December

SP - 1

EP - 5

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

PB - Institute of Electrical and Electronics Engineers Inc.

ER -