research-article

Experimental Analysis of Streaming Algorithms for Graph Partitioning

Authors:

M. Tamer ÖzsuAuthors Info & Claims

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

Pages 1375 - 1392

https://doi.org/10.1145/3299869.3300076

Published: 25 June 2019 Publication History

Abstract

We report a systematic performance study of streaming graph partitioning algorithms. Graph partitioning plays a crucial role in overall system performance as it has a significant impact on both load balancing and inter-machine communication. The streaming model for graph partitioning has recently gained attention due to its ability to scale to very large graphs with limited resources. The main objective of this study is to understand how the choice of graph partitioning algorithm affects system performance, resource usage and scalability. We focus on both offline graph analytics and online graph query workloads. The study considers both edge-cut and vertex-cut approaches. Our results show that the no partitioning algorithms performs best in all cases, and the choice of graph partitioning algorithm depends on: (i) type and degree distribution of the graph, (ii) characteristics of the workloads, and (iii) specific application requirements.

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Cited By

Cui PLiu HJiang DTang BYuan Y(2025)Nezha: An Efficient Distributed Graph Processing System on Heterogeneous HardwareProceedings of the ACM on Management of Data10.1145/37097073:1(1-27)Online publication date: 11-Feb-2025
https://doi.org/10.1145/3709707
Fang PLi ZKhan ALuo SWang FShi ZFeng D(2025)Information-Oriented Random Walks and Pipeline Optimization for Distributed Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342433337:1(408-422)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3424333
Ning BGao Y(2025)Efficient Partitioning Algorithms for Optimizing Big Graph ComputationComputing and Combinatorics10.1007/978-981-96-1093-8_17(202-213)Online publication date: 20-Feb-2025
https://doi.org/10.1007/978-981-96-1093-8_17
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Index Terms

Experimental Analysis of Streaming Algorithms for Graph Partitioning
1. Computing methodologies
  1. Parallel computing methodologies
    1. Parallel algorithms
      1. Vector / streaming algorithms
2. Information systems
  1. Data management systems
    1. Database administration
      1. Database performance evaluation
    2. Database design and models
      1. Graph-based database models

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cover image ACM Conferences

SIGMOD '19: Proceedings of the 2019 International Conference on Management of Data

June 2019

2106 pages

ISBN:9781450356435

DOI:10.1145/3299869

General Chairs:
Peter Boncz
CWI & Vrije Universiteit Amsterdam, The Netherlands
,
Stefan Manegold
CWI & Universiteit Leiden, The Netherlands
,
Program Chairs:
Anastasia Ailamaki
EPFL, Switzerland
,
Amol Deshpande
University of Maryland, USA
,
Tim Kraska
MIT, USA

Copyright © 2019 ACM.

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Published: 25 June 2019

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SIGMOD/PODS '19

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SIGMOD/PODS '19: International Conference on Management of Data

June 30 - July 5, 2019

Amsterdam, Netherlands

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SIGMOD '19 Paper Acceptance Rate 88 of 430 submissions, 20%;

Overall Acceptance Rate 785 of 4,003 submissions, 20%

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https://doi.org/10.1145/3709707
Fang PLi ZKhan ALuo SWang FShi ZFeng D(2025)Information-Oriented Random Walks and Pipeline Optimization for Distributed Graph EmbeddingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.342433337:1(408-422)Online publication date: Jan-2025
https://doi.org/10.1109/TKDE.2024.3424333
Ning BGao Y(2025)Efficient Partitioning Algorithms for Optimizing Big Graph ComputationComputing and Combinatorics10.1007/978-981-96-1093-8_17(202-213)Online publication date: 20-Feb-2025
https://doi.org/10.1007/978-981-96-1093-8_17
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