RealGraph: A graph engine leveraging the power-law distribution of real-world graphs
- Authors
- Jo, Yong-Yeon; Kim, Sang-Wook; Jang, Myung-Hwan; Park, Sunju
- Issue Date
- May-2019
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- Graph engine; Power-law degree distribution; Real-world graph; Single machine
- Citation
- The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp.807 - 817
- Indexed
- SCOPUS
- Journal Title
- The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
- Start Page
- 807
- End Page
- 817
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147840
- DOI
- 10.1145/3308558.3313434
- 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.
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