Cohesive Co-Evolution Patterns 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 : We focus on the discovery of interesting patterns in dynamic attributed graphs. To this end, we define the novel problem of mining cohesive co-evolution patterns. Briefly speaking, cohesive co-evolution patterns are tri-sets of vertices, timestamps, and signed attributes that describe the local co-evolutions of similar vertices at several timestamps according to set of signed attributes that express attributes trends. We design the first algorithm to mine the complete set of cohesive co-evolution patterns in a dynamic graph. Some experiments performed on both synthetic and real-world datasets demonstrate that our algorithm enables to discover relevant patterns in a feasible time.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-01353051
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Submitted on : Wednesday, August 10, 2016 - 4:20:31 PM
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Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut. Cohesive Co-Evolution Patterns in Dynamic Attributed Graphs. Discovery Science - 15th International Conference (DS 2012), Oct 2012, Lyon France. pp.110-124, ⟨10.1007/978-3-642-33492-4_11⟩. ⟨hal-01353051⟩

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