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Graph-based substructure pattern mining with edge-weight

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Abstract

To represent complex inter-relationships among entities, weighted graphs are more useful than their unweighted counterparts. In a transactional graph setting, researchers have made several attempts to mine weighted frequent subgraphs from a collection of edge-weighted graphs, which will serve as the representative feature of the underlying graph database and can be further used for analysis. As weighted support of any pattern does not hold downward closure property, a property that is often used in frequent pattern mining to control search space, has made weighted frequent substructure mining a tremendously difficult task. This article proposes an efficient weighted frequent subgraph mining framework called WFSM-MaxPWS for graphs with static edge weights. We introduce a new pruning technique called MaxPWS pruning along with canonical labeling of subgraphs, which helps reduce the search space significantly without compromising completeness. Extending the WFSM-MaxPWS framework, we propose another framework called DewgSpan that is capable of mining graphs with dynamic edge weight. DewgSpan utilizes a summarized edge-weight distribution table to overcome the new challenges of dynamic edge-weight settings. Evaluation results show that WFSM-MaxPWS and DewgSpan are significantly faster than the existing MaxW pruning technique of weighted pattern mining.

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Data Availability

The data that support the finding of this study is available at https://github.com/cseduashraful/graphdatasets

Notes

  1. https://pubchem.ncbi.nlm.nih.gov/bioassay/167

  2. https://github.com/cseduashraful/graphdatasets

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Acknowledgements

Out of the two frameworks discussed in this article, a preliminary version of the framework for static edge-weighted substructure mining has been previously published in PAKDD 2018 [50].

Funding

This work is partially supported by (a) University of Dhaka, (b) Natural Sciences and Engineering Research Council of Canada (NSERC), and (c) University of Manitoba.

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Authors and Affiliations

  1. University of Dhaka, Dhaka, Bangladesh

    Md. Ashraful Islam, Chowdhury Farhan Ahmed & Md. Tanvir Alam

  2. University of Manitoba, Winnipeg, Manitoba, Canada

    Carson Kai-Sang Leung

Authors
  1. Md. Ashraful Islam
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  2. Chowdhury Farhan Ahmed
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  3. Md. Tanvir Alam
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  4. Carson Kai-Sang Leung
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Contributions

Conceptualization: Md. Ashraful Islam & Chowdhury Farhan Ahmed Methodology: Md. Ashraful Islam Formal analysis and investigation: Md. Ashraful Islam, Chowdhury Farhan Ahmed & Md. Tanvir Alam Writing - original draft preparation: Md. Ashraful Islam & Md. Tanvir Alam Writing - review and editing: Chowdhury Farhan Ahmed & Carson Kai-Sang Leung Supervision: Chowdhury Farhan Ahmed & Carson Kai-Sang Leung

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Correspondence to Md. Ashraful Islam.

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Islam, M.A., Ahmed, C.F., Alam, M.T. et al. Graph-based substructure pattern mining with edge-weight. Appl Intell 54, 3756–3785 (2024). https://doi.org/10.1007/s10489-024-05356-7

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