Discovering Patterns in Time-Varying Graphs: A Triclustering Approach

TL;DR

This paper presents a hyper-parameter-free triclustering method for analyzing time-varying graphs, enabling automatic detection of evolving structures across vertices and time segments.

Contribution

The novel approach infers time segments directly from edge distribution evolution, eliminating the need for prior time quantization or hyper-parameter tuning.

Findings

Effective on artificial data demonstrating good behavior.

Shows potential for exploratory analysis of real-world data.

Automatically infers meaningful time segments.

Abstract

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.