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A High-Performance Graph Engine for Efficient Social Network Analysis

Authors:

Yong-Yeon Jo,

Myung-Hwan Jang,

Hyungsoo Jung,

Sang-Wook KimAuthors Info & Claims

WWW '18: Companion Proceedings of the The Web Conference 2018

Pages 61 - 62

https://doi.org/10.1145/3184558.3186929

Published: 23 April 2018 Publication History

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Abstract

Existing single-machine based graph engines do not leverage the characteristic of social networks following the power-law degree distribution. We propose a new graph engine tailored for processing and analyzing large-scale social networks efficiently by exploiting the power-law degree property

References

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W. Han et al. 2013. TurboGraph: a fast parallel graph engine handling billion-scale graphs in a single PC. In Proceedings of ACM SIGKDD. 77--85.

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X. Zhu et al. 2015 b. GridGraph: large-scale graph processing on a single machine using 2-Level hierarchical partitioning. In Proceedings of USENIX ATC. 375--386.

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Y. Jo et al. 2016. Data locality in graph engines: implications and preliminary experimental results Proceedings of ACM CIKM. 1885--1888.

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Cited By

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Mei JSun SLi CXu CChen CLiu YWang JZhao CHou XGuo MHe BCong X(2024)FlowWalker: A Memory-Efficient and High-Performance GPU-Based Dynamic Graph Random Walk FrameworkProceedings of the VLDB Endowment10.14778/3659437.365943817:8(1788-1801)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659438
Chen NLiu ALiu GXu JZhao L(2021)Representation Learning Based Query Decomposition for Batch Shortest Path Processing in Road NetworksService-Oriented Computing10.1007/978-3-030-91431-8_16(257-272)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-91431-8_16

Index Terms

A High-Performance Graph Engine for Efficient Social Network Analysis
1. Information systems
  1. Data management systems
    1. Database design and models
      1. Graph-based database models

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Information & Contributors

Information

Published In

WWW '18: Companion Proceedings of the The Web Conference 2018

April 2018

2023 pages

ISBN:9781450356404

General Chairs:
Pierre-Antoine Champin
Université Claude Bernard Lyon 1, France
,
Fabien Gandon
Inria, Université Côte d'Azur, CNRS, I3S, France
,
Lionel Médini
Université Claude Bernard Lyon 1, CNRS, LIRIS, France
,
Program Chairs:
Mounia Lalmas
Spotify, UK
,
Panagiotis G. Ipeirotis
New York University, USA

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]

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

Publication History

Published: 23 April 2018

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Poster

Funding Sources

National Research Foundation of Korea (NRF) grant funded by the Ministry of Science and ICT (MSIT)
e Information Technology Research Center (ITRC) support program
Semiconductor Industry Collaborative Project between Hanyang University and Samsung Electronics Co. Ltd.
Next-Generation Information Computing Development Program through NRF funded by MSIT

Conference

WWW '18

Sponsor:

IW3C2

WWW '18: The Web Conference 2018

April 23 - 27, 2018

Lyon, France

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

View all

Mei JSun SLi CXu CChen CLiu YWang JZhao CHou XGuo MHe BCong X(2024)FlowWalker: A Memory-Efficient and High-Performance GPU-Based Dynamic Graph Random Walk FrameworkProceedings of the VLDB Endowment10.14778/3659437.365943817:8(1788-1801)Online publication date: 1-Apr-2024
https://dl.acm.org/doi/10.14778/3659437.3659438
Chen NLiu ALiu GXu JZhao L(2021)Representation Learning Based Query Decomposition for Batch Shortest Path Processing in Road NetworksService-Oriented Computing10.1007/978-3-030-91431-8_16(257-272)Online publication date: 22-Nov-2021
https://dl.acm.org/doi/10.1007/978-3-030-91431-8_16

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