Mining exceptional closed patterns in attributed graphs

Anes Bendimerad 1 Marc Plantevit 1 Céline Robardet 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : Geo-located social media provide a large amount of information describing urban areas based on user descriptions and comments. Such data makes possible to identify meaningful city neighborhoods on the basis of the footprints left by a large and diverse population that uses this type of media. In this paper, we present some methods to exhibit the predominant activities and their associated urban areas to automatically describe a whole city. Based on a suitably attributed graph model, our approach identifies neighborhoods with homogeneous and exceptional characteristics. We introduce the novel problem of exceptional subgraph mining in attributed graphs and propose a complete algorithm that takes benefits from closure operators, new upper bounds and pruning properties. We also define an approach to sample the space of closed exceptional subgraphs within a given time-budget. Experiments performed on 10 real datasets are reported and demonstrate the relevancy of both approaches, and also show their limits.
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Anes Bendimerad, Marc Plantevit, Céline Robardet. Mining exceptional closed patterns in attributed graphs. Knowledge and Information Systems (KAIS), Springer, 2018, 56 (1), pp.1 - 25. ⟨10.1007/s10115-017-1109-2⟩. ⟨hal-01625007⟩

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