Many applications see huge demands of discovering important 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. © 2013 Springer-Verlag.
CITATION STYLE
Desmier, E., Plantevit, M., Robardet, C., & Boulicaut, J. F. (2013). Trend mining in dynamic attributed graphs. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8188 LNAI, pp. 654–669). https://doi.org/10.1007/978-3-642-40988-2_42
Mendeley helps you to discover research relevant for your work.