RealGraph: A graph engine leveraging the power-law distribution of real-world graphs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jo, Yong-Yeon | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.contributor.author | Jang, Myung-Hwan | - |
dc.contributor.author | Park, Sunju | - |
dc.date.accessioned | 2022-07-09T15:03:06Z | - |
dc.date.available | 2022-07-09T15:03:06Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2019-05 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/147840 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | RealGraph: A graph engine leveraging the power-law distribution of real-world graphs | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3308558.3313434 | - |
dc.identifier.scopusid | 2-s2.0-85066901739 | - |
dc.identifier.wosid | 000483508400076 | - |
dc.identifier.bibliographicCitation | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019, pp.807 - 817 | - |
dc.relation.isPartOf | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | - |
dc.citation.title | The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 | - |
dc.citation.startPage | 807 | - |
dc.citation.endPage | 817 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Theory & Methods | - |
dc.subject.keywordPlus | Graphic methods | - |
dc.subject.keywordPlus | Scheduling algorithms | - |
dc.subject.keywordPlus | World Wide Web | - |
dc.subject.keywordPlus | Block based | - |
dc.subject.keywordPlus | Power law degree distribution | - |
dc.subject.keywordPlus | Power law distribution | - |
dc.subject.keywordPlus | Real-world datasets | - |
dc.subject.keywordPlus | Real-world graphs | - |
dc.subject.keywordPlus | Single- machines | - |
dc.subject.keywordPlus | Work-load distribution | - |
dc.subject.keywordPlus | Workload allocation | - |
dc.subject.keywordPlus | Engines | - |
dc.subject.keywordAuthor | Graph engine | - |
dc.subject.keywordAuthor | Power-law degree distribution | - |
dc.subject.keywordAuthor | Real-world graph | - |
dc.subject.keywordAuthor | Single machine | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3308558.3313434 | - |
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