research-article

Reduction of large-scale graphs: : Effective edge shedding at a controllable ratio under resource constraints

Authors:

Ying ZhangAuthors Info & Claims

Volume 240, Issue C

https://doi.org/10.1016/j.knosys.2022.108126

Published: 15 March 2022 Publication History

Abstract

As technology advances, many complicated systems can be represented by networks/graphs. However, when using limited computing resources such as portable computers or personal desktop computers, users are not able to store and mine large-scale graphs due to the unparalleled growth of the amount of data we generate. In order to address this challenge, we present effective edge shedding. Effective edge shedding can reduce the amount of data to be processed and the corresponding storage space while speeding up graph algorithms and queries, thereby supporting interactive analysis, helping knowledge discovery, and eliminating noise. In this paper, to extract the underlying features of a graph, we present two effective edge shedding methods on the basis of preserving the expected vertex degree. Both methods allow users to control the edge shedding process, thus generating a reduced graph of the predefined size based on the computing resource constraint. Using four real-world datasets in different domains, we performed an extensive experimental evaluation of our methods and compared them with the state-of-the-art graph summarization method on seven graph analysis tasks. The results indicate that our methods can achieve up to 58.6% higher accuracy on graph analysis tasks compared with the state-of-the-art method. For very large datasets, our methods consumes only 0.3% of the running time of the competitive method when generating the reduced graph. The above results fully illustrate the advantages of our methods.

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Cited By

Wang YWu JYu ZHu JZhou Q(2023)A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenariosEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107091126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107091
Su ZKurths JMeyerhenke HAlhajj RAgarwal NMa ZRokne JAn JCharalampos CMagdy W(2022)Network Sparsification via Degree- and Subgraph-Based Edge SamplingProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM55673.2022.10068651(9-16)Online publication date: 10-Nov-2022
https://dl.acm.org/doi/10.1109/ASONAM55673.2022.10068651

Index Terms

Reduction of large-scale graphs: Effective edge shedding at a controllable ratio under resource constraints
1. Information systems
  1. Information systems applications
    1. Data mining
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Knowledge-Based Systems

Knowledge-Based Systems Volume 240, Issue C

Mar 2022

830 pages

ISSN:0950-7051

Issue’s Table of Contents

Elsevier B.V.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 15 March 2022

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Cited By

Wang YWu JYu ZHu JZhou Q(2023)A structurally re-parameterized convolution neural network-based method for gearbox fault diagnosis in edge computing scenariosEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.107091126:PCOnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.107091
Su ZKurths JMeyerhenke HAlhajj RAgarwal NMa ZRokne JAn JCharalampos CMagdy W(2022)Network Sparsification via Degree- and Subgraph-Based Edge SamplingProceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1109/ASONAM55673.2022.10068651(9-16)Online publication date: 10-Nov-2022
https://dl.acm.org/doi/10.1109/ASONAM55673.2022.10068651

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