T-ARC: Topology-Aware Randomized Clustering via Distributionally Robust Stochastic Block Models
In this work, we introduce a new clustering method, namely T-ARC (Topology-Aware Randomized Clustering), that corrects the geometric bias of K-means by embedding topological information directly into the optimization objective. Building on the assumption that the data admits an underlying hidden structure modeled via a latent graph, the idea is to uncover this information through the interplay between the standard K-means data-fidelity term and a graph-cut penalty, which discourages cluster assignments inconsistent with the connectivity structure of the data. To render this coupling tractable, the latent graph is modeled as a random realization from a Stochastic Block Model (SBM), whose s...
| Authors | Serena Grazia De Benedictis, Andersen Ang, Nicoletta Del Buono et al. |
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| Primary category | cs.LG |
| Published | 30 September 2026 |
| Last updated at source | 5 October 2026 |
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