Machine Learning

, Volume 106, Issue 8, pp 1171–1211 | Cite as

Exceptional contextual subgraph mining

  • Mehdi Kaytoue
  • Marc Plantevit
  • Albrecht Zimmermann
  • Anes Bendimerad
  • Céline Robardet


Many relational data result from the aggregation of several individual behaviors described by some characteristics. For instance, a bike-sharing system may be modeled as a graph where vertices stand for bike-share stations and connections represent bike trips made by users from one station to another. Stations and trips are described by additional information such as the description of the geographical environment of the stations (business vs. residential area, closeness to POI, elevation, urbanization density, etc.), or properties of the bike trips (timestamp, user profile, weather, events and other special conditions about the trip). Identifying highly connected components (such as communities or quasi-cliques) in this graph provides interesting insights into global usages but does not capture mobility profiles that characterize a subpopulation. To tackle this problem we propose an approach rooted in exceptional model mining to find exceptional contextual subgraphs, i.e., subgraphs generated from a context or a description of the individual behaviors that is exceptional (behaves in a different way) compared to the whole augmented graph. The dependency between a context and an edge is assessed by a \(\chi ^2\) test and the weighted relative accuracy measure is used to only retain contexts that strongly characterize connected subgraphs. We present an original algorithm that uses sophisticated pruning techniques to restrict the search space of vertices, context refinements, and edges to be considered. An experimental evaluation on synthetic data and two real-life datasets demonstrates the effectiveness of the proposed pruning mechanisms, as well as the relevance of the discovered patterns.


Attributed graphs Exceptional Model Mining Subgroup discovery Supervised pattern mining 



The authors would like to thank the anonymous reviewers for their frank, fruitful, constructive and insightful comments and the authors of the MiMaG and DSSD algorithms for providing us their prototypes. They also gratefully acknowledge Pierre Houdyer for the development of the pattern visualization platform on VELOV data. This work has been partially supported by the projects GRAISearch (FP7-PEOPLE-2013-IAPP) and VEL’INNOV (ANR INOV 2012).


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Copyright information

© The Author(s) 2017

Authors and Affiliations

  • Mehdi Kaytoue
    • 1
  • Marc Plantevit
    • 2
  • Albrecht Zimmermann
    • 1
  • Anes Bendimerad
    • 1
  • Céline Robardet
    • 1
  1. 1.CNRS, LIRIS UMR5205INSA de LyonLyonFrance
  2. 2.CNRS, LIRIS UMR5205Université Lyon 1LyonFrance

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