arXiv Statistics ML

Exact Community Recovery in Bipartite Networks

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....

By Behzad Aalipur, Yichen Qin

efficiency

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...

By Huan Qing, Jingli Wang

efficiency

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.

By Sie Hendrata Dharmawan, Peter Chin

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.

By Shahin Momenzadeh, Rojiar Pir Mohammadiani

rag benchmarks

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.

By John Park, Ning Hao

rag

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.

By Jingyun Zhang, Hao Peng, Jianxin Li, Angsheng Li, Philip S. Yu

benchmarks

arXiv Machine Learning

Aug 13

Spectral graph clustering with inhomogeneous latent geometry

arXiv:2608. 11321v1 Announce Type: cross Abstract: We study spectral clustering in the presence of a confounding latent geometry.

By Konstantin Avrachenkov, Lucas S. Sibemberg, Alexander Van Werde

arXiv Machine Learning

Jun 2

Query-Limited Community Recovery in Stochastic Block Models

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.

By Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen, Suhas Thejaswi

benchmarks

arXiv Machine Learning

Jul 3

Provably Finding a Hidden Dense Submatrix among Many Planted Dense Submatrices via Convex Programming

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.

By Valentine Olanubi (University of Alabama, Department of Mathematics), Phineas Agar (University of Alabama, Department of Mathematics), Brendan Ames (University of Southampton, School of Mathematical Sciences)

safety

arXiv Machine Learning

Jun 2

Flexible Online Representation Learning Based on Similarity Matching

arXiv:2606. 01546v1 Announce Type: new Abstract: Sparse high-dimensional representations are conducive to uncovering nontrivial structures in unsupervised exploration of data.

By Shagesh Sridharan, Yanis Bahroun, Anirvan M. Sengupta

arXiv Machine Learning

Jun 19

Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities

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.

By Alexandra Carpentier, Christophe Giraud, Nicolas Verzelen

arXiv Machine Learning

Aug 18

Correlation Clustering with Random Partial Information

arXiv:2608. 16315v1 Announce Type: cross Abstract: Correlation clustering is a fundamental unsupervised learning problem.

By Rajath Rao K. N., Jens Schl\"oter, Sami Davies, Amira Ouchene, Yasamin Nazari

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