RealGraph: A graph engine leveraging the power-law distribution of real-world graphs

Yong Yeon Jo; Sang Wook Kim; Myung Hwan Jang; Sun Ju Park

doi:10.1145/3308558.3313434

RealGraph: A graph engine leveraging the power-law distribution of real-world graphs

Yong Yeon Jo, Sang Wook Kim, Myung Hwan Jang, Sun Ju Park

Department of Business Administration

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

As the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the power-law degree distribution of real-world graphs seriously. Two problems have been observed when existing graph engines process real-world graphs: inefficient scanning of the sparse indicator and the delay in iteration progress due to uneven workload distribution. In this paper, we propose RealGraph, a single-machine based graph engine equipped with the hierarchical indicator and the block-based workload allocation. Experimental results on real-world datasets show that RealGraph significantly outperforms existing graph engines in terms of both speed and scalability.

Original language	English
Title of host publication	The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
Publisher	Association for Computing Machinery, Inc
Pages	807-817
Number of pages	11
ISBN (Electronic)	9781450366748
DOIs	https://doi.org/10.1145/3308558.3313434
Publication status	Published - 2019 May 13
Event	2019 World Wide Web Conference, WWW 2019 - San Francisco, United States Duration: 2019 May 13 → 2019 May 17

Publication series

Name	The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

Conference

Conference	2019 World Wide Web Conference, WWW 2019
Country/Territory	United States
City	San Francisco
Period	19/5/13 → 19/5/17

Bibliographical note

Funding Information:
This work was supported by (1) the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (NRF-2017R1A2B3004581) and (2) Next-Generation Information Computing Development Program through NRF funded by MSIT (NRF-2017M3C4A7069440). Also, we appreciate Samsung Electronics' university program [Flash Solutions for Emerging Applications] that significantly helps train our lab. members.

Publisher Copyright:
© 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Software

Access to Document

10.1145/3308558.3313434

Cite this

Jo, Y. Y., Kim, S. W., Jang, M. H., & Park, S. J. (2019). RealGraph: A graph engine leveraging the power-law distribution of real-world graphs. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 (pp. 807-817). (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). Association for Computing Machinery, Inc. https://doi.org/10.1145/3308558.3313434

Jo, Yong Yeon ; Kim, Sang Wook ; Jang, Myung Hwan ; Park, Sun Ju. / RealGraph : A graph engine leveraging the power-law distribution of real-world graphs. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. pp. 807-817 (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).

@inproceedings{9b42d122bbef4c77b2a58357453d84d4,

title = "RealGraph: A graph engine leveraging the power-law distribution of real-world graphs",

abstract = "As the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the power-law degree distribution of real-world graphs seriously. Two problems have been observed when existing graph engines process real-world graphs: inefficient scanning of the sparse indicator and the delay in iteration progress due to uneven workload distribution. In this paper, we propose RealGraph, a single-machine based graph engine equipped with the hierarchical indicator and the block-based workload allocation. Experimental results on real-world datasets show that RealGraph significantly outperforms existing graph engines in terms of both speed and scalability.",

author = "Jo, {Yong Yeon} and Kim, {Sang Wook} and Jang, {Myung Hwan} and Park, {Sun Ju}",

note = "Funding Information: This work was supported by (1) the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (NRF-2017R1A2B3004581) and (2) Next-Generation Information Computing Development Program through NRF funded by MSIT (NRF-2017M3C4A7069440). Also, we appreciate Samsung Electronics' university program [Flash Solutions for Emerging Applications] that significantly helps train our lab. members. Publisher Copyright: {\textcopyright} 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.; 2019 World Wide Web Conference, WWW 2019 ; Conference date: 13-05-2019 Through 17-05-2019",

year = "2019",

month = may,

day = "13",

doi = "10.1145/3308558.3313434",

language = "English",

series = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",

publisher = "Association for Computing Machinery, Inc",

pages = "807--817",

booktitle = "The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019",

}

Jo, YY, Kim, SW, Jang, MH & Park, SJ 2019, RealGraph: A graph engine leveraging the power-law distribution of real-world graphs. in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, Association for Computing Machinery, Inc, pp. 807-817, 2019 World Wide Web Conference, WWW 2019, San Francisco, United States, 19/5/13. https://doi.org/10.1145/3308558.3313434

RealGraph : A graph engine leveraging the power-law distribution of real-world graphs. / Jo, Yong Yeon; Kim, Sang Wook; Jang, Myung Hwan; Park, Sun Ju.

The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc, 2019. p. 807-817 (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - RealGraph

T2 - 2019 World Wide Web Conference, WWW 2019

AU - Jo, Yong Yeon

AU - Kim, Sang Wook

AU - Jang, Myung Hwan

AU - Park, Sun Ju

N1 - Funding Information: This work was supported by (1) the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT (MSIT) (NRF-2017R1A2B3004581) and (2) Next-Generation Information Computing Development Program through NRF funded by MSIT (NRF-2017M3C4A7069440). Also, we appreciate Samsung Electronics' university program [Flash Solutions for Emerging Applications] that significantly helps train our lab. members. Publisher Copyright: © 2019 IW3C2 (International World Wide Web Conference Committee), published under Creative Commons CC-BY 4.0 License.

PY - 2019/5/13

Y1 - 2019/5/13

N2 - As the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the power-law degree distribution of real-world graphs seriously. Two problems have been observed when existing graph engines process real-world graphs: inefficient scanning of the sparse indicator and the delay in iteration progress due to uneven workload distribution. In this paper, we propose RealGraph, a single-machine based graph engine equipped with the hierarchical indicator and the block-based workload allocation. Experimental results on real-world datasets show that RealGraph significantly outperforms existing graph engines in terms of both speed and scalability.

AB - As the size of real-world graphs has drastically increased in recent years, a wide variety of graph engines have been developed to deal with such big graphs efficiently. However, the majority of graph engines have been designed without considering the power-law degree distribution of real-world graphs seriously. Two problems have been observed when existing graph engines process real-world graphs: inefficient scanning of the sparse indicator and the delay in iteration progress due to uneven workload distribution. In this paper, we propose RealGraph, a single-machine based graph engine equipped with the hierarchical indicator and the block-based workload allocation. Experimental results on real-world datasets show that RealGraph significantly outperforms existing graph engines in terms of both speed and scalability.

UR - http://www.scopus.com/inward/record.url?scp=85066901739&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85066901739&partnerID=8YFLogxK

U2 - 10.1145/3308558.3313434

DO - 10.1145/3308558.3313434

M3 - Conference contribution

AN - SCOPUS:85066901739

T3 - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

SP - 807

EP - 817

BT - The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019

PB - Association for Computing Machinery, Inc

Y2 - 13 May 2019 through 17 May 2019

ER -

Jo YY, Kim SW, Jang MH, Park SJ. RealGraph: A graph engine leveraging the power-law distribution of real-world graphs. In The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019. Association for Computing Machinery, Inc. 2019. p. 807-817. (The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019). https://doi.org/10.1145/3308558.3313434

RealGraph: A graph engine leveraging the power-law distribution of real-world graphs

Abstract

Publication series

Conference

Bibliographical note

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this