Deep Autoencoder-like Nonnegative Matrix Factorization for Community Detection

2018 ◽  
Author(s):  
Fanghua Ye ◽  
Chuan Chen ◽  
Zibin Zheng
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix

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.

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.

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