Trend Mining in Dynamic Attributed Graphs

Elise Desmier 1 Marc Plantevit 1 Céline Robardet 1 Jean-François Boulicaut 1
1 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : 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.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01339225
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Submitted on : Wednesday, June 29, 2016 - 3:49:29 PM
Last modification on : Thursday, November 21, 2019 - 2:24:15 AM

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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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