scholarly journals Community Detection Algorithm Based on Nonnegative Matrix Factorization and Improved Density Peak Clustering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 5749-5759 ◽  
Author(s):  
Hong Lu ◽  
Xiaoshuang Sang ◽  
Qinghua Zhao ◽  
Jianfeng Lu
Keyword(s):  
Community Detection ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Detection Algorithm ◽  
Density Peak ◽  
Density Peak Clustering ◽  
Community Detection Algorithm

scholarly journals Community Detection Based on Density Peak Clustering Model and Multiple Attribute Decision-Making Strategy TOPSIS

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jianjun Cheng ◽  
Xu Wang ◽  
Wenshuang Gong ◽  
Jun Li ◽  
Nuo Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Community Detection ◽  
Detection Algorithm ◽  
Multiple Attribute Decision Making ◽  
Density Peak ◽  
Clustering Model ◽  
Multiple Attribute ◽  
Density Peak Clustering ◽  
Community Detection Algorithm ◽  
Topsis Technique

Community detection is one of the key research directions in complex network studies. We propose a community detection algorithm based on a density peak clustering model and multiple attribute decision-making strategy, TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution). First, the two-dimensional dataset, which is transformed from the network by taking the density and distance as the attributes of nodes, is clustered by using the DBSCAN algorithm, and outliers are determined and taken as the key nodes. Then, the initial community frameworks are formed and expanded by adding the most similar node of the community as its new member. In this process, we use TOPSIS to cohesively integrate four kinds of similarities to calculate an index, and use it as a criterion to select the most similar node. Then, we allocate the nonkey nodes that are not covered in the expanded communities. Finally, some communities are merged to obtain a stable partition in two ways. This paper designs some experiments for the algorithm on some real networks and some synthetic networks, and the proposed method is compared with some popular algorithms. The experimental results testify for the effectiveness and show the accuracy of our algorithm.

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.

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