We propose a provable randomized framework in which a clustering algorithm is applied to a graph's adjacency matrix generated from a stochastic block model. A ...

We propose a provable randomized framework in which a clustering algorithm is applied to a graph's adjacency matrix generated from a stochastic block model. A ...

Bibliographic details on Randomized Robust matrix Completion for the Community Detection Problem.

May 25, 2018 · We provide analysis and numerical results using a convex clustering algorithm based on matrix completion. Subjects: Social and Information ...

Paper Title: Randomized Robust Matrix Completion for the Community Detection Problem ; Authors: Adel Karimian; University of Central Florida.

In this paper, a mixture antlion optimization and k-median for solving the community detection problem is proposed and named as K-median Modularity ALO.

Dec 4, 2022 · Abstract—This article explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a.

Jul 5, 2018 · Randomized Robust Matrix Completion for the Community Detection Problem. Authors: Adel Karimian, Mostafa Rahmani, Andre Beckus, George Atia ...

This paper explores and analyzes the unsupervised clustering of large partially observed graphs. We propose a scalable and provable randomized framework for ...

The objective is to prove that spectral methods are robust to this type of noise, even if they are agnostic to the presence (or not) of the random graph. We ...