skip to main content
10.1145/3299869.3320219acmconferencesArticle/Chapter ViewAbstractPublication PagesmodConference Proceedingsconference-collections
research-article

Large Scale Graph Mining with G-Miner

Published: 25 June 2019 Publication History

Abstract

This Demo presents G-Miner, a distributed system for graph mining. The take-aways for Demo attendees are: (1) a good understanding of the challenges of various graph mining workloads; (2) useful insights on how to design a good system for graph mining by comparing G-Miner with existing systems on performance, expressiveness and user-friendliness; and (3) how to use G-Miner for interactive graph analytics.

References

[1]
Hongzhi Chen, Miao Liu, Yunjian Zhao, Xiao Yan, Da Yan, and James Cheng. 2018. G-Miner: an efficient task-oriented graph mining system. In Proceedings of the Thirteenth EuroSys Conference. ACM, 32.
[2]
Grzegorz Malewicz, Matthew H Austern, Aart JC Bik, James C Dehnert, Ilan Horn, Naty Leiser, and Grzegorz Czajkowski. 2010. Pregel: a system for large-scale graph processing. In SIGMOD. ACM, 135--146.
[3]
Frank McSherry, Michael Isard, and Derek Gordon Murray. 2015. Scalability! But at what COST?. In HotOS.
[4]
Abdul Quamar, Amol Deshpande, and Jimmy Lin. 2016. NScale: neighborhood-centric large-scale graph analytics in the cloud. The VLDB Journal 25, 2 (2016), 125--150.
[5]
Carlos HC Teixeira, Alexandre J Fonseca, Marco Serafini, Georgos Siganos, Mohammed J Zaki, and Ashraf Aboulnaga. 2015. Arabesque: a system for distributed graph mining. In SOSP. ACM, 425--440.
[6]
Da Yan, Hongzhi Chen, James Cheng, M Tamer Özsu, Qizhen Zhang, and John Lui. 2017. G-thinker: big graph mining made easier and faster. arXiv preprint arXiv:1709.03110 (2017).
[7]
Da Yan, Yuanyuan Tian, and James Cheng. 2017. Systems for Big Graph Analytics. Springer.

Cited By

View all
  • (2023)CompressGraph: Efficient Parallel Graph Analytics with Rule-Based CompressionProceedings of the ACM on Management of Data10.1145/35886841:1(1-31)Online publication date: 30-May-2023
  • (2022)VSGMProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.5555/3571885.3571954(1-15)Online publication date: 13-Nov-2022
  • (2022)G-tranProceedings of the VLDB Endowment10.14778/3551793.355181315:11(2545-2558)Online publication date: 1-Jul-2022
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

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
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. distributed system
  2. large-scale graph mining

Qualifiers

  • Research-article

Funding Sources

  • General Research Fund from the Hong Kong RGC
  • Innovation and Technology Fund from the Government of the Hong Kong

Conference

SIGMOD/PODS '19
Sponsor:
SIGMOD/PODS '19: International Conference on Management of Data
June 30 - July 5, 2019
Amsterdam, Netherlands

Acceptance Rates

SIGMOD '19 Paper Acceptance Rate 88 of 430 submissions, 20%;
Overall Acceptance Rate 785 of 4,003 submissions, 20%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)10
  • Downloads (Last 6 weeks)1
Reflects downloads up to 16 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2023)CompressGraph: Efficient Parallel Graph Analytics with Rule-Based CompressionProceedings of the ACM on Management of Data10.1145/35886841:1(1-31)Online publication date: 30-May-2023
  • (2022)VSGMProceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis10.5555/3571885.3571954(1-15)Online publication date: 13-Nov-2022
  • (2022)G-tranProceedings of the VLDB Endowment10.14778/3551793.355181315:11(2545-2558)Online publication date: 1-Jul-2022
  • (2022)VSGM: View-Based GPU-Accelerated Subgraph Matching on Large GraphsSC22: International Conference for High Performance Computing, Networking, Storage and Analysis10.1109/SC41404.2022.00057(1-15)Online publication date: Nov-2022
  • (2021)Vertex-Centric Visual Programming for Graph Neural NetworksProceedings of the 2021 International Conference on Management of Data10.1145/3448016.3452770(2803-2807)Online publication date: 9-Jun-2021
  • (2021)SeastarProceedings of the Sixteenth European Conference on Computer Systems10.1145/3447786.3456247(359-375)Online publication date: 21-Apr-2021
  • (2021)Accelerating graph sampling for graph machine learning using GPUsProceedings of the Sixteenth European Conference on Computer Systems10.1145/3447786.3456244(311-326)Online publication date: 21-Apr-2021
  • (2019)GrasperProceedings of the ACM Symposium on Cloud Computing10.1145/3357223.3362715(87-100)Online publication date: 20-Nov-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media