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Cohesive Co-Evolution Patterns in Dynamic Attributed Graphs
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.
https://hal.archives-ouvertes.fr/hal-01353051
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Submitted on : Wednesday, August 10, 2016 - 4:20:31 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
Contributor : Équipe Gestionnaire Des Publications Si Liris Connect in order to contact the contributor
Submitted on : Wednesday, August 10, 2016 - 4:20:31 PM
Last modification on : Tuesday, June 1, 2021 - 2:08:09 PM
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