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A Graph Representative Structure for Detecting Automorphic Graphs

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dc.contributor.author Hlaing, Yu Wai
dc.contributor.author Oo, Kyaw May
dc.date.accessioned 2019-08-09T07:11:15Z
dc.date.available 2019-08-09T07:11:15Z
dc.date.issued 2015
dc.identifier.uri http://onlineresource.ucsy.edu.mm/handle/123456789/1985
dc.description.abstract Graphs are prevalently used to model the relationships between objects in various domains. Storing the graphs into large databases is a challenging task as it deals with efficient space and time management. Unlike item sets in huge transactional databases, it becomes essential to ensure the consistency of graph databases since relationships among edges of a graph are predominant. One of the necessary procedures required is a mechanism to check whether two graphs are automorphic(duplicated) or not. Difficulty in identifying and eliminating the automorphic graphs is a challenging problem to the research community. In this paper, we propose a graph representative structure that is called graph code. There are three main phases: preprocessing, code generation and code matching. In preprocessing phase, vertex list, edge list and adjacent edge information are generated for input graph. In code generation, edge dictionary plays an important role. The edge dictionary and adjacent edge information are used to generate graph codes. In code matching, the new graph code is compared with those of other graphs in graph dataset to determine whether they are automorphic or not. The experimental results and comparisons offer a positive response to the proposed structure. en_US
dc.language.iso en en_US
dc.publisher 9th International Conference on Genetic and Evolutionary Computing (ICGEC2015) en_US
dc.relation.ispartofseries ;pp.189-197
dc.subject edge dictionary en_US
dc.subject graph automorphism en_US
dc.subject graph code en_US
dc.subject graph representative structure en_US
dc.title A Graph Representative Structure for Detecting Automorphic Graphs en_US
dc.type Article en_US

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