Discovering Patterns in Time-Varying Graphs: A Triclustering Approach
This addresses the challenge of analyzing dynamic graph data for researchers in network analysis, though it appears incremental as it builds on existing clustering methods.
The paper tackles the problem of tracking structures in time-varying graphs by introducing a triclustering approach that simultaneously segments source vertices, destination vertices, and time without hyper-parameter tuning, with experiments on artificial data showing good behavior and real-life data demonstrating potential for exploratory analysis.
This paper introduces a novel technique to track structures in time varying graphs. The method uses a maximum a posteriori approach for adjusting a three-dimensional co-clustering of the source vertices, the destination vertices and the time, to the data under study, in a way that does not require any hyper-parameter tuning. The three dimensions are simultaneously segmented in order to build clusters of source vertices, destination vertices and time segments where the edge distributions across clusters of vertices follow the same evolution 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 any a priori quantization. Experiments conducted on artificial data illustrate the good behavior of the technique, and a study of a real-life data set shows the potential of the proposed approach for exploratory data analysis.