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Communication Dans Un Congrès Année : 2013

Trend Mining in Dynamic Attributed Graphs

Marc Plantevit
Céline Robardet
Jean-François Boulicaut

Résumé

Many applications see huge demands of discovering impor- tant patterns in dynamic attributed graph. In this paper, we introduce the problem of discovering trend sub-graphs in dynamic attributed graphs. This new kind of pattern relies on the graph structure and the temporal evolution of the attribute values. Several interestingness measures are introduced to focus on the most relevant patterns with regard to the graph structure, the vertex attributes, and the time. We design an efficient algorithm that benefits from various constraint properties and provide an extensive empirical study from several real-world dynamic attributed graphs.

Dates et versions

hal-01339225 , version 1 (29-06-2016)

Identifiants

Citer

Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut. Trend Mining in Dynamic Attributed Graphs. Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Sep 2013, Prague, Czech Republic. pp.654-669, ⟨10.1007/978-3-642-40988-2_42⟩. ⟨hal-01339225⟩
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