Graph-XLL: a graph library for extra large graph analytics on a single machine

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dc.contributor.authorWu, Jian
dc.contributor.supervisorThomo, Alex
dc.contributor.supervisorSrinivasan, Venkatesh
dc.date.accessioned2019-08-26T21:53:23Z
dc.date.available2019-08-26T21:53:23Z
dc.date.copyright2019en_US
dc.date.issued2019-08-26
dc.degree.departmentDepartment of Computer Scienceen_US
dc.degree.levelMaster of Science M.Sc.en_US
dc.description.abstractGraph libraries containing already-implemented algorithms are highly desired since users can conveniently use the algorithms off-the-shelf to achieve fast analyt- ics and prototyping, rather than implementing the algorithms with lower-level APIs. Besides the ease of use, the ability to efficiently process extra large graphs is also required by users. The popular existing graph libraries include the igraph R library and the NetworkX Python library. Although these libraries provide many off-the-shelf algorithms for users, the in-memory graph representation limits their scalability for computing on large graphs. Therefore, in this work, we develop Graph-XLL: a graph library implemented using the WebGraph framework in a vertex-centric manner, with much less memory requirement compared to igraph and NetworkX. Scalable analytics for extra large graphs (up to tens of millions of vertices and billions of edges) can be achieved on a single consumer grade machine within a reasonable amount of time. Such computation would cause out-of-memory error if using igraph or NetworkX.en_US
dc.description.scholarlevelGraduateen_US
dc.identifier.bibliographicCitation“Graph-XLL: a Graph Library for Extra Large Graph Analytics on a Single Machine”, Jian Wu, Venkatesh Srinivasan, and Alex Thomo, IISA 2019en_US
dc.identifier.bibliographicCitation“Fast Truss Decomposition in Large-scale Probabilistic Graphs”, Fatemeh Es- fahani, Jian Wu, Venkatesh Srinivasan, Alex Thomo, and Kui Wu, EDBT 2019en_US
dc.identifier.bibliographicCitation“K-Truss Decomposition of Large Networks on a Single Consumer-Grade Machine”, Jian Wu, Alison Goshulak, Venkatesh Srinivasan, and Alex Thomo, ASONAM 2018en_US
dc.identifier.urihttp://hdl.handle.net/1828/11067
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.subjectGraph Analyticsen_US
dc.subjectWebGraphen_US
dc.subjectGraph Libraryen_US
dc.titleGraph-XLL: a graph library for extra large graph analytics on a single machineen_US
dc.typeThesisen_US

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