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  1. University of Computer Studies, Yangon
  2. Faculty of Computer Science

A Graph Representative Structure for Detecting Automorphic Graphs

http://hdl.handle.net/20.500.12678/0000004240
http://hdl.handle.net/20.500.12678/0000004240
7f3fb621-37c9-4924-9854-d9a1002deb7a
3cf978d6-b884-45c1-876b-17e56447348c
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5.A 5.A Graph Representative Structure for Detecting Automorphic Graphs.pdf (384 Kb)
Publication type
Article
Upload type
Publication
Title
Title A Graph Representative Structure for Detecting Automorphic Graphs
Language en
Publication date 2015
Authors
Hlaing, Yu Wai
Oo, Kyaw May
Description
Graphs are prevalently used to model the relationships between objects in various domains. Storing the graphs into large databases is a challengingtask as it deals with efficient space and time management. Unlike item sets inhuge transactional databases, it becomes essential to ensure the consistency ofgraph databases since relationships among edges of a graph are predominant.One of the necessary procedures required is a mechanism to check whether twographs 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 calledgraph code. There are three main phases: preprocessing, code generation andcode matching. In preprocessing phase, vertex list, edge list and adjacent edgeinformation are generated for input graph. In code generation, edge dictionaryplays an important role. The edge dictionary and adjacent edge information areused 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 areautomorphic or not. The experimental results and comparisons offer a positiveresponse to the proposed structure.
Keywords
edge dictionary, graph automorphism, graph code, graph representative structure
Identifier http://onlineresource.ucsy.edu.mm/handle/123456789/1985
Journal articles
9th International Conference on Genetic and Evolutionary Computing (ICGEC2015)
Conference papers
Books/reports/chapters
Thesis/dissertations
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