arXiv Statistics ML
Exact Community Recovery in Bipartite Networks
Read the original on arXiv Statistics ML →The Flow has not summarised this story yet — read it at arXiv Statistics ML.
Related stories
arXiv Machine Learning
3d ago
Exact Recovery by Neighborhood Smoothing in Directed Stochastic Block Models
arXiv:2601.16427v3 Announce Type: replace-cross Abstract: We study exact community recovery in sparse directed stochastic block models using neighborhood smoothing of connection-probability profiles....
arXiv Machine Learning
Sep 10
Directed mixed membership stochastic blockmodel
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 Machine Learning
Jun 25
Simplify to Amplify: Achieving Information-Theoretic Bounds with Fewer Steps in Spectral Community Detection
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 Machine Learning
Jun 9
Alcmean's: Unsupervised community detection using local Laplacian, automatic detection of the number of centers
arXiv:2606. 09100v1 Announce Type: cross Abstract: Community detection is a fundamental problem in the analysis of complex networks.
arXiv Machine Learning
Aug 12
Spectral Embeddings of Degree-$\alpha$ Laplacians in Random Dot Product Graphs
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 AI
Jul 8
Breaking Structural Isolation: Scalable Graph Clustering via Community-Aware Sampling and Structural Entropy
arXiv:2607. 05469v1 Announce Type: cross Abstract: Unsupervised graph clustering is a fundamental technique for uncovering underlying semantic patterns in large-scale networks.