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...
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...
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:2602. 17104v2 Announce Type: replace-cross Abstract: We propose a streamlined spectral algorithm for community detection in the two-community stochastic block model (SBM) under constant edge density assumptions.
arXiv:2606. 02055v1 Announce Type: cross Abstract: We study exact community recovery in the two-community stochastic block model on $n$ vertices under limited and noisy access to network data.
arXiv:2601. 03946v3 Announce Type: replace-cross Abstract: We consider the densest submatrix problem, which seeks the submatrix of fixed size of a given binary matrix that contains the most nonzero entries.
arXiv:2509. 15822v3 Announce Type: replace-cross Abstract: Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number $K$ of communities is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold.
arXiv:2606. 14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference.
arXiv:2511. 11927v2 Announce Type: replace-cross Abstract: Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations.
arXiv:2607. 14304v1 Announce Type: cross Abstract: We study sparse random geometric graphs generated by connecting pairs of high-dimensional vectors whose inner product exceeds a threshold.
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.
The paper introduces the Marcus mapping, an extension of Marcus theorem that allows certain sparse symmetric matrices to be transformed into doubly stochastic symmetric matrices via diagonal matrices. Leveraging this mapping, the authors propose the Doubly Stochastic Adaptive Neighbors Clustering algorithm (ANCMM), which incorporates rank constraints to ensure the learned similarity graph naturally partitions into the desired number of clusters. Experiments demonstrate ANCMM’s effectiveness compared to state‑of‑the‑art methods, and the authors also establish a connection between the Marcus mapping and a specific optimal transport problem.