Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
The Flow has not summarised this story yet — read it at arXiv Machine Learning.
arXiv:2609.12445v1 Announce Type: cross Abstract: Community detection in bipartite networks is a fundamental problem in modern data analysis, with applications in recommendation systems, biological n...
The paper investigates clustering of multi‑layer networks generated under a mixture multi‑layer stochastic block model (MMSBM). It derives the minimax optimal clustering error rate, expressed via Chernoff information between component edge‑probability distributions, and introduces a two‑stage estimator that achieves this rate even under extreme sparsity. The method is extended to count‑weighted networks using a mixture multi‑layer Poisson block model, with numerical experiments and real‑data analyses demonstrating its practical performance.
arXiv:2101.02307v4 Announce Type: replace-cross Abstract: Mixed membership modeling for undirected networks has been extensively explored in network science over the past few years. Despite the subst...
The paper introduces T-ARC, a clustering algorithm that integrates topological information into the K‑means objective by coupling a data‑fidelity term with a graph‑cut penalty. The latent graph is modeled as a random realization from a Stochastic Block Model, whose parameter is optimized via Distributionally Robust Optimization, using a persistence‑based similarity matrix derived from zero‑dimensional persistent homology. Experiments on synthetic non‑convex data and Fashion‑MNIST subsets demonstrate that T‑ARC recovers latent topological structures and outperforms K‑means on curved and interleaved clusters while remaining competitive and more stable on real data.
arXiv:2608. 10845v1 Announce Type: cross Abstract: Spectral clustering methods for network data are commonly based on a few matrix representations, such as the adjacency matrix and the symmetric Laplacian.
arXiv:2610.18937v1 Announce Type: cross Abstract: Network-linked data combine a network with covariates and responses observed for its nodes. Common regression approaches either model dependence amon...