A Triclustering Approach for Time Evolving Graphs
This addresses the need for automated structure tracking in dynamic graphs for researchers in network analysis, though it appears incremental as it builds on existing co-clustering methods.
The paper tackles the problem of tracking structures in time-evolving graphs by introducing a parameter-free triclustering technique that simultaneously segments source vertices, target vertices, and time based on similar edge distributions. Experiments on synthetic data demonstrate good performance, and a real-life dataset study shows potential for exploratory data analysis.
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.