2012 | OriginalPaper | Buchkapitel
Cohesive Co-evolution Patterns in Dynamic Attributed Graphs
verfasst von : Elise Desmier, Marc Plantevit, Céline Robardet, Jean-François Boulicaut
Erschienen in: Discovery Science
Verlag: Springer Berlin Heidelberg
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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.