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.
|Title of host publication||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Publisher||Association for Computing Machinery, Inc|
|Number of pages||11|
|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
|Name||The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019|
|Conference||2019 World Wide Web Conference, WWW 2019|
|Period||19/5/13 → 19/5/17|
Bibliographical noteFunding 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.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications