Year: 2017
Type: article
Abstract: 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.
Authors: Ritu Yadav, Samarth Varshney
Institution: National Institute of Technology Warangal
Language: English
Topic: Graph Theory and Algorithms
Subfield: Computational Theory and Mathematics
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W2765151997