2012 IEEE 12th International Conference on Data Mining Workshops (2012)
Brussels, Belgium Belgium
Dec. 10, 2012 to Dec. 10, 2012
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2012.61
This paper introduces a novel technique to track structures in time evolving graphs. The method is based on a parameter free approach for three-dimensional co-clustering of the source vertices, the target vertices and the time. All these features are simultaneously segmented in order to build time segments and clusters of vertices whose edge distributions are similar and evolve in the same way over the time segments. The main novelty of this approach lies in that the time segments are directly inferred from the evolution of the edge distribution between the vertices, thus not requiring the user to make an a priori discretization. Experiments conducted on a synthetic dataset illustrate the good behaviour of the technique, and a study of a real-life dataset shows the potential of the proposed approach for exploratory data analysis.
Image edge detection, Image segmentation, Noise, Computational modeling, Clustering algorithms, Social network services, Standards, Model Selection, Coclustering, Blockmodeling, Graph Mining
R. Guigoures, M. Boulle and F. Rossi, "A Triclustering Approach for Time Evolving Graphs," 2012 IEEE 12th International Conference on Data Mining Workshops(ICDMW), Brussels, Belgium Belgium, 2012, pp. 115-122.