Symmetric Nonnegative Matrix Factorization-Based Community Detection Models and Their Convergence Analysis

2021 ◽  
pp. 1-13
Author(s):  
Xin Luo ◽  
Zhigang Liu ◽  
Long Jin ◽  
Yue Zhou ◽  
MengChu Zhou
Keyword(s):  
Convergence Analysis ◽  
Community Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Symmetric Nonnegative Matrix Factorization

Temporal community detection based on symmetric nonnegative matrix factorization

2017 ◽  
Vol 31 (13) ◽  
pp. 1750102 ◽  
Author(s):  
Pengfei Jiao ◽  
Haodong Lyu ◽  
Xiaoming Li ◽  
Wei Yu ◽  
Wenjun Wang
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Essential Property ◽  
Evolving Networks ◽  
Gradient Descent Algorithm ◽  
Symmetric Nonnegative Matrix Factorization ◽  
Internal Evolution

To understand time-evolving networks, researchers should not only concentrate on the community structures, an essential property of complex networks, in each snapshot, but also study the internal evolution of the entire networks. Temporal communities provide insights into such mechanism, i.e., how the communities emerge, expand, shrink, merge, split and decay over time. Based on the symmetric nonnegative matrix factorization (SNMF), we present a dynamic model to detect temporal communities, which not only could find a well community structure in a given snapshot but also demands the results bear some similarity to the partition obtained from the previous snapshot. Moreover, our method can handle the situation that of the number of community changes in the networks. Also, a gradient descent algorithm is proposed to optimize the objective function of the model. Experimental results on both the synthetic and real-world networks indicate that our method outperforms the state-of-art methods for temporal community detection.

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.

scholarly journals MHSNMF: multi-view hessian regularization based symmetric nonnegative matrix factorization for microbiome data analysis

2020 ◽  
Vol 21 (S6) ◽  
Author(s):  
Yuanyuan Ma ◽  
Junmin Zhao ◽  
Yingjun Ma
Keyword(s):  
Data Analysis ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Rapid Development ◽  
Nonnegative Matrix ◽  
Metabolomics Data ◽  
Normalized Mutual Information ◽  
Microbiome Data ◽  
Symmetric Nonnegative Matrix Factorization ◽  
Microbiome Data Analysis

Abstract Background With the rapid development of high-throughput technique, multiple heterogeneous omics data have been accumulated vastly (e.g., genomics, proteomics and metabolomics data). Integrating information from multiple sources or views is challenging to obtain a profound insight into the complicated relations among micro-organisms, nutrients and host environment. In this paper we propose a multi-view Hessian regularization based symmetric nonnegative matrix factorization algorithm (MHSNMF) for clustering heterogeneous microbiome data. Compared with many existing approaches, the advantages of MHSNMF lie in: (1) MHSNMF combines multiple Hessian regularization to leverage the high-order information from the same cohort of instances with multiple representations; (2) MHSNMF utilities the advantages of SNMF and naturally handles the complex relationship among microbiome samples; (3) uses the consensus matrix obtained by MHSNMF, we also design a novel approach to predict the classification of new microbiome samples. Results We conduct extensive experiments on two real-word datasets (Three-source dataset and Human Microbiome Plan dataset), the experimental results show that the proposed MHSNMF algorithm outperforms other baseline and state-of-the-art methods. Compared with other methods, MHSNMF achieves the best performance (accuracy: 95.28%, normalized mutual information: 91.79%) on microbiome data. It suggests the potential application of MHSNMF in microbiome data analysis. Conclusions Results show that the proposed MHSNMF algorithm can effectively combine the phylogenetic, transporter, and metabolic profiles into a unified paradigm to analyze the relationships among different microbiome samples. Furthermore, the proposed prediction method based on MHSNMF has been shown to be effective in judging the types of new microbiome samples.

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