International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 175 - Issue 5 |
Published: Oct 2017 |
Authors: Ritu Yadav, Samarth Varshney |
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Ritu Yadav, Samarth Varshney . A Method of Subgraphs Extraction in a Large Graph Database in a Distributed System. International Journal of Computer Applications. 175, 5 (Oct 2017), 1-5. DOI=10.5120/ijca2017914960
@article{ 10.5120/ijca2017914960, author = { Ritu Yadav,Samarth Varshney }, title = { A Method of Subgraphs Extraction in a Large Graph Database in a Distributed System }, journal = { International Journal of Computer Applications }, year = { 2017 }, volume = { 175 }, number = { 5 }, pages = { 1-5 }, doi = { 10.5120/ijca2017914960 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2017 %A Ritu Yadav %A Samarth Varshney %T A Method of Subgraphs Extraction in a Large Graph Database in a Distributed System%T %J International Journal of Computer Applications %V 175 %N 5 %P 1-5 %R 10.5120/ijca2017914960 %I Foundation of Computer Science (FCS), NY, USA
Since many real applications such as web connectivity, social networks, and so on, are emerging now-a-days, thus graph databases have been commonly used as significant tools to exemplify and query complex graph data wherein each vertex in a graph usually contains information, which can be modeled by a set of tokens or elements. The method for subgraphs extraction by considering set similarity query over a large graph database has already been proposed, which retrieves subgraphs that are structurally isomorphic to the query graph, and meanwhile satisfy the condition of vertex pair matching with the (dynamic/fixed) weighted set similarity in a centralized system. This paper explains the efficient implementation of subgraphs extraction in a large graph database in a distributed environment by considering both vertex set similarity and graph topology which offers a better price/performance ratio and increases availability using redundancy when parts of a system fail than centralized systems in case of a large dataset (i.e., a graph with millions/billions of nodes wherein each node contains some information) by performing parallel processing.