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GraphMDL : sélection de motifs de graphes avec le principe MDL

Abstract : Many graph pattern mining algorithms have been designed to identify recurring structures in graphs. The main drawback of these approaches is that they often extract too many patterns for human analysis. Recently, pattern mining methods using the Minimum Description Length (MDL) principle have been proposed to select a characteristic subset of patterns from transactional, sequential and relational data. In this paper, we propose a MDL-based approach for selecting a characteristic subset of patterns on labeled graphs. A key notion in this paper is the introduction of ports to encode connections between pattern occurrences without any loss of information. Experiments show that the number of patterns is drastically reduced, and the selected patterns can have complex shapes.
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Submitted on : Wednesday, March 18, 2020 - 5:35:43 PM
Last modification on : Wednesday, November 3, 2021 - 8:15:01 AM
Long-term archiving on: : Friday, June 19, 2020 - 2:37:46 PM


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  • HAL Id : hal-02511412, version 1


Francesco Bariatti, Peggy Cellier, Sébastien Ferré. GraphMDL : sélection de motifs de graphes avec le principe MDL. EGC 2020, Jan 2020, Bruxelles, Belgique. ⟨hal-02511412⟩



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