Network Clustering for Latent State and Changepoint Detection
This method addresses the need for automated network clustering in scenarios with evolving or multi-aspect data, but it appears incremental as it builds on existing convex techniques without broad validation.
The authors tackled the problem of clustering multiple networks to identify common structural patterns without pre-specifying the number of clusters, by proposing a convex approach with a fusion penalty that yields a tree-like structure, and they demonstrated its effectiveness on synthetic data.
Network models provide a powerful and flexible framework for analyzing a wide range of structured data sources. In many situations of interest, however, multiple networks can be constructed to capture different aspects of an underlying phenomenon or to capture changing behavior over time. In such settings, it is often useful to cluster together related networks in attempt to identify patterns of common structure. In this paper, we propose a convex approach for the task of network clustering. Our approach uses a convex fusion penalty to induce a smoothly-varying tree-like cluster structure, eliminating the need to select the number of clusters a priori. We provide an efficient algorithm for convex network clustering and demonstrate its effectiveness on synthetic examples.