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 | |
| 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 | United States |
| City | San Francisco |
| Period | 19/5/13 → 19/5/17 |
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All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Software
Cite this
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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 - A graph engine leveraging the power-law distribution of real-world graphs
AU - Jo, Yong Yeon
AU - Kim, Sang Wook
AU - Jang, Myung Hwan
AU - Park, Sun Ju
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
ER -