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A Graph Representative Structure for Detecting Automorphic Graphs

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Genetic and Evolutionary Computing

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 387))

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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.

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Author information

Authors and Affiliations

  1. University of Computer Studies, Yangon, Myanmar

    Yu Wai Hlaing

  2. University of Information Technology, Yangon, Myanmar

    Kyaw May Oo

Authors
  1. Yu Wai Hlaing
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  2. Kyaw May Oo
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Corresponding author

Correspondence to Yu Wai Hlaing .

Editor information

Editors and Affiliations

  1. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Thi Thi Zin

  2. School of Computer Science and Tech..., Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China

    Jerry Chun-Wei Lin

  3. College of Information Science and Engg, Fujian University of Technology, Fuzhou, China

    Jeng-Shyang Pan

  4. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Pyke Tin

  5. Faculty of Engineering, University of Miyazaki, Miyazaki, Japan

    Mitsuhiro Yokota

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© 2016 Springer International Publishing Switzerland

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Hlaing, Y.W., Oo, K.M. (2016). A Graph Representative Structure for Detecting Automorphic Graphs. In: Zin, T., Lin, JW., Pan, JS., Tin, P., Yokota, M. (eds) Genetic and Evolutionary Computing. Advances in Intelligent Systems and Computing, vol 387. Springer, Cham. https://doi.org/10.1007/978-3-319-23204-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-23204-1_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23203-4

  • Online ISBN: 978-3-319-23204-1

  • eBook Packages: EngineeringEngineering (R0)

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