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An efficient and scalable approach for mining subgraphs in a single large graph

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Abstract

In many recent applications, a graph is used to simulate many complex systems, such as social networks, traffic models or bioinformatics, and the underlying graphs for these systems are very large. Algorithms for mining all frequent subgraphs from a single large graph have attracted much attention and been studied in more detail lately. Mining frequent subgraphs is important, and defined as finding all subgraphs whose occurrences in a dataset are greater than or equal to a given frequency threshold. Among frequent subgraph algorithms, GraMi is considered as the state-of-the-art approach. However, GraMi has a huge search space, and therefore still needs a lot of time and memory to process a large graph. In this paper, we propose two effective strategies to balance and reduce the search space of GraMi, which can decrease the number of candidate subgraphs generated, with early pruning of a large portion of the domain for each candidate. Our experiments were performed on four real datasets and the results show that the performance of our balancing GraMi is better than those of the original algorithm GraMi and the optimized version SoGraMi.

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Acknowledgements

This work was supported by Institute for Computational Science and Technology (ICST) – Ho Chi Minh City and the Department of Science and Technology (DOST) – Ho Chi Minh City under grant no. 23/2021/HĐ-QKHCN.

We are especially thankful to Mohammed Elseidy, who provided the GraMi source code and two datasets, MiCo and CiteSeer.

Author information

Authors and Affiliations

  1. Faculty of Information and Communications, Kien Giang University, Kien Giang, Vietnam

    Lam B. Q. Nguyen

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Lam B. Q. Nguyen

  3. School of Computer Science and Engineering, International University, Ho Chi Minh City, Vietnam

    Loan T. T. Nguyen

  4. Vietnam National University, Ho Chi Minh City, Vietnam

    Loan T. T. Nguyen

  5. Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam

    Bay Vo

  6. Faculty of Electrical and Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Ivan Zelinka

  7. Department of Computer Science, FEI, VŠB-Technical University of Ostrava, Tr. 17. Listopadu 15, Ostrava, Czech Republic

    Ivan Zelinka

  8. Department of Computing, Mathematics, and Physics, Western Norway University of Applied Sciences, Bergen, Norway

    Jerry Chun-Wei Lin

  9. Department of Computer Engineering, Sejong University, Seoul, Republic of Korea

    Unil Yun

  10. Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warsaw, Poland

    Hung Son Nguyen

Authors
  1. Lam B. Q. Nguyen
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  2. Loan T. T. Nguyen
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  3. Bay Vo
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  4. Ivan Zelinka
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Correspondence to Bay Vo.

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Nguyen, L.B.Q., Nguyen, L.T.T., Vo, B. et al. An efficient and scalable approach for mining subgraphs in a single large graph. Appl Intell 52, 17881–17895 (2022). https://doi.org/10.1007/s10489-022-03164-5

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