Node Clustering of Time-Varying Graphs Based on Temporal Label Smoothness

We propose a node clustering method for time-varying graphs based on the assumption that the cluster labels are changed smoothly over time. Clustering is one of the fundamental tasks in machine learning, data mining, and signal processing. Although most existing studies focus on the clustering of nodes in static graphs, we often encounter time-varying graphs for time-series data, e.g., social networks, brain functional connectivity, and point clouds. In this paper, we formulate a clustering of nodes in time-varying graphs as an optimization problem based on spectral clustering, with a smoothness constraint of the node labels. Experiments on synthetic and real-world time-varying graphs are conducted to validate the effectiveness of the proposed approach.