Graph-Based Fraud Detection with the Free Energy Distance

Sylvain Courtain*, Bertrand Lebichot, Ilkka Kivimäki, Marco Saerens

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


This paper investigates a real-world application of the free energy distance between nodes of a graph [14, 20] by proposing an improved extension of the existing Fraud Detection System named APATE [36]. It relies on a new way of computing the free energy distance based on paths of increasing length, and scaling on large, sparse, graphs. This new approach is assessed on a real-world large-scale e-commerce payment transactions dataset obtained from a major Belgian credit card issuer. Our results show that the free-energy based approach reduces the computation time by one half while maintaining state-of-the art performance in term of Precision@100 on fraudulent card prediction.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications VIII - Volume 2 Proceedings of the 8th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019
EditorsHocine Cherifi, Sabrina Gaito, José Fernendo Mendes, Esteban Moro, Luis Mateus Rocha
Number of pages13
Publication statusPublished - 2020
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Complex Networks and their Applications - Lisbon, Portugal
Duration: 10 Dec 201912 Dec 2019
Conference number: 8

Publication series

NameStudies in Computational Intelligence
Volume882 SCI
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


ConferenceInternational Conference on Complex Networks and their Applications
Abbreviated titleComplex Networks
Internet address


  • Credit card fraud detection
  • Free energy distance
  • Network data analysis
  • Network science
  • Semi-supervised learning

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