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. © 2012 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Desmier, E., Plantevit, M., Robardet, C., & Boulicaut, J. F. (2012). Cohesive co-evolution patterns in dynamic attributed graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7569 LNAI, pp. 110–124). https://doi.org/10.1007/978-3-642-33492-4_11
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