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 |
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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 |
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Conference
Conference | 2019 World Wide Web Conference, WWW 2019 |
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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