Extraction de motifs pour la détection d’anomalies dans des graphes: application à la fraude dans les marchés publics

Lucas Potin (), Rosa Figueiredo (), Vincent Labatut () and Christine Largeron ()
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Lucas Potin: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Rosa Figueiredo: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Vincent Labatut: LIA - Laboratoire Informatique d'Avignon - AU - Avignon Université - Centre d'Enseignement et de Recherche en Informatique - CERI
Christine Largeron: LabHC - Laboratoire Hubert Curien - IOGS - Institut d'Optique Graduate School - UJM - Université Jean Monnet - Saint-Étienne - CNRS - Centre National de la Recherche Scientifique

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Abstract: In the context of public procurement, several indicators, called red flags, are used to estimate fraud risk. These red flags are calculated according to certain contract attributes and are therefore dependant on the proper filling of the award notices. In this paper, we propose a general framework based on pattern extraction to detect anomalous graphs. It aims to identify subgraph patterns associated with the presence of red flags, in order to construct a set of new red flag indicators. These patterns can then be used in cases where red flags information is missing. Our experiments show that most of the red flags can be retrieved with a small percentage of patterns.

Keywords: Pattern Mining; Attributed Graphs; Public procurement; Recherche de patterns; Graphes attribués; Marchés publics (search for similar items in EconPapers)
Date: 2023-01-16
Note: View the original document on HAL open archive server: https://hal.science/hal-03930668
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Published in Extraction et Gestion des Connaissances (EGC), Jan 2023, Lyon, France. pp.289-296

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-03930668

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