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An updated dashboard of complete search FSM implementations in centralized graph transaction databases

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Abstract

Frequent subgraph mining algorithms are widely used in various areas for information analysis. As yet, a handful of algorithms have been proposed and defined in the literature. While several experimental studies were reported, these experiments lack critical information which are important for selecting an implementation of an algorithm for a specific case of use. In this paper, we report on experiments that we carried out on available implementations of complete search Frequent Subgraph Mining (FSM) algorithms. These experiments are conducted in order to choose a suitable FSM solution (i.e., implementation). We identified 32 algorithms in the literature, six of them were selected for our experiments, through a filtering process relying on a set of criteria. Thirteen working implementations of these 6 algorithms are experimented. In this paper, we provide details of the experiments in terms of performance metrics and input variation effect. We propose a preliminary selection of the most efficient FSM solutions for end users, based on the most tested centralized graph-transaction datasets of the literature.

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Notes

  1. Complete search in centralized graph transaction databases.

  2. CAIR home page: www.irit.fr/CAIR.

  3. Copyright is with the authors. Published in the Proceedings of the BDA 2016 Conference: bda2016.ensma.fr/program.html.

  4. Supergraphs are the graphs containing g.

  5. A simple graph is “an un-weighted and undirected graph with no loops and no multiple links between any two distinct nodes” (Gibbons 1985)

  6. studies with keyword ’Frequent Subgraph Mining’

  7. The list of algorithms is based on our search of FSM studies until March 2016

  8. From public references or by contacting authors

  9. 1 request and 2 reminders have been sent to authors.

  10. Original ParMol github.com/yangyi0318/MyParMol/tree/master/ParMol

  11. Please refer to liris.cnrs.fr/rihab.ayed/ESFSM.pdf for more details.

  12. We collected the references of datasets from FSM papers or by contacting authors that used them (March 2016)

  13. Please refer to perso.liris.cnrs.fr/rihab.ayed/DFSM.pdf for dataset links.

  14. For more details, please refer to perso.liris.cnrs.fr/rihab.ayed/DFSM.pdf

  15. We tested the effect of our modifications on performance.

  16. For all results, see liris.cnrs.fr/rihab.ayed/DFSM.pdf

  17. Please note that the number of frequent subgraphs evolves inversely to the support (MST): this is due to the anti-monotone property of the support (Grahne et al. 2000).

  18. Authors tried to explain this difference in their tests.

  19. Please refer to liris.cnrs.fr/rihab.ayed/ESFSM.pdf for more detailed results of gSpan solutions.

  20. Please refer to liris.cnrs.fr/rihab.ayed/ESFSM.pdf for more detailed results of Gaston solutions.

  21. We verified that failure at low support values is due to a lack of memory (by resorting to a machine with 128 GB of memory).

  22. Please refer to liris.cnrs.fr/rihab.ayed/ESFSM.pdf for more details.

  23. No abortion at the beginning of the execution.

  24. Please refer to liris.cnrs.fr/rihab.ayed/DFSM.pdf for all results of Gaston ParMol

  25. The repository can be accessed by contacting us

  26. CAIR home page: https://www.irit.fr/CAIR/fr/.

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Acknowledgements

This work was elaborated as a part of the CAIRFootnote 26 project. Special thanks are addressed to FSM authors and particularly to Xifeng Yan, Thorsten Meinl, Andrés Gago-Alonso, Christian Borgelt, Mohammad Al Hasan and Sabeur Aridhi for sending us software, datasets, providing us with some clarifications, as well as for their availability.

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  1. Université de Lyon, CNRS, Université Lyon 1, LIRIS UMR5205, Villeurbanne, 69622, France

    Rihab Ayed, Mohand-Saïd Hacid & Rafiqul Haque

  2. Université de Carthage, Ecole Polytechnique de Tunisie, SERCOM, INSAT, Tunis, 1080, Tunisia

    Abderrazak Jemai

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This research is performed within the scope of the CAIR (Contextual and Aggregated Information Retrieval) project that is funded by ANR (Agence Nationale de la Recherche) grant ANR-14-CE23-0006 -www.irit.fr/CAIR

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Ayed, R., Hacid, MS., Haque, R. et al. An updated dashboard of complete search FSM implementations in centralized graph transaction databases. J Intell Inf Syst 55, 149–182 (2020). https://doi.org/10.1007/s10844-019-00579-4

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