Adaptive Overlapping Community Detection with Bayesian NonNegative Matrix Factorization

2017 ◽  
pp. 339-353 ◽  
Author(s):  
Xiaohua Shi ◽  
Hongtao Lu ◽  
Guanbo Jia
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Overlapping Community Detection ◽  
Overlapping Community

scholarly journals An Overlapping Community Detection Approach in Ego-Splitting Networks Using Symmetric Nonnegative Matrix Factorization

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 869
Author(s):  
Mingqing Huang ◽  
Qingshan Jiang ◽  
Qiang Qu ◽  
Abdur Rasool
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Large Scale ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Overlapping Community Detection ◽  
Detection Approach ◽  
Overlapping Community ◽  
Large Scale Networks ◽  
Symmetric Nonnegative Matrix Factorization

Overlapping clustering is a fundamental and widely studied subject that identifies all densely connected groups of vertices and separates them from other vertices in complex networks. However, most conventional algorithms extract modules directly from the whole large-scale graph using various heuristics, resulting in either high time consumption or low accuracy. To address this issue, we develop an overlapping community detection approach in Ego-Splitting networks using symmetric Nonnegative Matrix Factorization (ESNMF). It primarily divides the whole network into many sub-graphs under the premise of preserving the clustering property, then extracts the well-connected sub-sub-graph round each community seed as prior information to supplement symmetric adjacent matrix, and finally identifies precise communities via nonnegative matrix factorization in each sub-network. Experiments on both synthetic and real-world networks of publicly available datasets demonstrate that the proposed approach outperforms the state-of-the-art methods for community detection in large-scale networks.

Community Detection Based on Modularized Deep Nonnegative Matrix Factorization

2020 ◽  
pp. 2159006
Author(s):  
Jin Huang ◽  
Tinghua Zhang ◽  
Weihao Yu ◽  
Jia Zhu ◽  
Ercong Cai
Keyword(s):  
Community Structure ◽  
Community Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Complex Data ◽  
Original Matrix ◽  
Latent Features ◽  
Topology Information ◽  
Structure Properties

Community detection is a well-established problem and nontrivial task in complex network analysis. The goal of community detection is to discover community structures in complex networks. In recent years, many existing works have been proposed to handle this task, particularly nonnegative matrix factorization-based method, e.g. HNMF, BNMF, which is interpretable and can learn latent features of complex data. These methods usually decompose the original matrix into two matrixes, in one matrix, each column corresponds to a representation of community and each column of another matrix indicates the membership between overall pairs of communities and nodes. Then they discover the community by updating the two matrices iteratively and learn the shallow feature of the community. However, these methods either ignore the topological structure characteristics of the community or ignore the microscopic community structure properties. In this paper, we propose a novel model, named Modularized Deep NonNegative Matrix Factorization (MDNMF) for community detection, which preserves both the topology information and the instinct community structure properties of the community. The experimental results show that our proposed models can significantly outperform state-of-the-art approaches on several well-known dataset.

Sign in / Sign up

Export Citation Format

Share Document