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Mining clique frequent approximate subgraphs from multi-graph collections

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Abstract

Recently, some algorithms have been reported for mining multi-graph Frequent Approximate Subgraphs (FASs). This kind of algorithm has a large applicability in social network analysis, image classification tasks and clustering, among others. However, all of them mine a large number of patterns. For this reason, some algorithms for mining a subset of FASs have been proposed. In this paper, we propose an efficient algorithm for mining a subset of FASs on multi-graph collections. Our proposed algorithm becomes an alternative for reducing the number of mined FASs by computing the clique FASs. It is important to highlight that, to the best of our knowledge; our proposal is the first algorithm for mining clique FASs on multi-graph collections. Our proposal is compared against other reported solutions and evaluated over several synthetic and real-world multi-graphs. In addition, we show the usefulness of the patterns mined by our proposal for image classification.

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Notes

  1. A graph G is a multi-graph if it can have more than one edge connecting the same pair of vertices (multi-edges) and edges connecting a vertex with itself (loops) [2, 21].

  2. A maximal FAS is a FAS that is not sub-isomorphic to another FAS [16].

  3. A closed FAS is a FAS that is not sub-isomorphic to another FAS with the same frequency [38].

  4. A clique FAS is a FAS such that every vertex is connected to every other by an edge [26].

  5. Multi-edges [2] are different edges connecting the same pair of vertices (i.e., e and \(e^{\prime } \) are multi-edges if \(e \neq e^{\prime }\) and \(\phi _{G}(e) = \phi _{G}(e^{\prime }) = \{u,v\}\) such that u, v ∈ VG, u≠v).

  6. A canonical form is a unique representation for isomorphic graphs [19, 39]

  7. PyGen is available in http://pywebgraph.sourceforge.net.

  8. http://www.csc.liv.ac.uk/∼frans/KDD/Software/ImageGenerator/imageGenerator.html

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Acknowledgements

This work was partly supported by the National Council of Science and Technology of Mexico (CONACyT) through the scholarship grant 287045.

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Authors and Affiliations

  1. Advanced Technologies Application Center (CENATAV), 7a ♯ 21406 e/ 214 and 216, Siboney, Playa, CP: 12200, Havana, Cuba

    Niusvel Acosta-Mendoza & Andrés Gago-Alonso

  2. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Luis Enrique Erro No. 1, Sta. María Tonantzintla, Puebla, CP: 72840, Mexico

    Jesús Ariel Carrasco-Ochoa & José Francisco Martínez-Trinidad

  3. Universidad de las Ciencias Informáticas (UCI), Carretera San Antonio, Km 2 1/2, Torrens, La Lisa, CP: 19370, Havana, Cuba

    José Eladio Medina-Pagola

Authors
  1. Niusvel Acosta-Mendoza
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  2. Jesús Ariel Carrasco-Ochoa
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  3. José Francisco Martínez-Trinidad
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  4. Andrés Gago-Alonso
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Correspondence to Niusvel Acosta-Mendoza.

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Acosta-Mendoza, N., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F. et al. Mining clique frequent approximate subgraphs from multi-graph collections. Appl Intell 50, 878–892 (2020). https://doi.org/10.1007/s10489-019-01564-8

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