Trend Mining in Dynamic Attributed Graphs
Abstract
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.
Cite
Text
Desmier et al. "Trend Mining in Dynamic Attributed Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013. doi:10.1007/978-3-642-40988-2_42Markdown
[Desmier et al. "Trend Mining in Dynamic Attributed Graphs." European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2013.](https://mlanthology.org/ecmlpkdd/2013/desmier2013ecmlpkdd-trend/) doi:10.1007/978-3-642-40988-2_42BibTeX
@inproceedings{desmier2013ecmlpkdd-trend,
title = {{Trend Mining in Dynamic Attributed Graphs}},
author = {Desmier, Elise and Plantevit, Marc and Robardet, Céline and Boulicaut, Jean-François},
booktitle = {European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases},
year = {2013},
pages = {654-669},
doi = {10.1007/978-3-642-40988-2_42},
url = {https://mlanthology.org/ecmlpkdd/2013/desmier2013ecmlpkdd-trend/}
}