scholarly journals Bayesian nonparametric clustering as a community detection problem

2020 ◽  
Vol 152 ◽  
pp. 107044
Author(s):  
Stefano F. Tonellato
Keyword(s):  
Community Detection ◽  
Bayesian Nonparametric ◽  
Detection Problem ◽  
Nonparametric Clustering

scholarly journals Community Detection Problem Based on Polarization Measures: An Application to Twitter: The COVID-19 Case in Spain

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 443
Author(s):  
Inmaculada Gutiérrez ◽  
Juan Antonio Guevara ◽  
Daniel Gómez ◽  
Javier Castro ◽  
Rosa Espínola
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Efficient Algorithm ◽  
Fuzzy Measure ◽  
The Political ◽  
Detection Problem ◽  
Additional Information ◽  
Political Position ◽  
Networks Analysis ◽  
Polarization Capacity

In this paper, we address one of the most important topics in the field of Social Networks Analysis: the community detection problem with additional information. That additional information is modeled by a fuzzy measure that represents the risk of polarization. Particularly, we are interested in dealing with the problem of taking into account the polarization of nodes in the community detection problem. Adding this type of information to the community detection problem makes it more realistic, as a community is more likely to be defined if the corresponding elements are willing to maintain a peaceful dialogue. The polarization capacity is modeled by a fuzzy measure based on the JDJpol measure of polarization related to two poles. We also present an efficient algorithm for finding groups whose elements are no polarized. Hereafter, we work in a real case. It is a network obtained from Twitter, concerning the political position against the Spanish government taken by several influential users. We analyze how the partitions obtained change when some additional information related to how polarized that society is, is added to the problem.

scholarly journals Optimizing community detection in social networks using antlion and K-median

2019 ◽  
Vol 8 (4) ◽  
Author(s):  
Amany A. Naem ◽  
Neveen I. Ghali
Keyword(s):  
Social Networks ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Problem ◽  
Real Life ◽  
Optimization Methods ◽  
Population Based ◽  
Detection Problem ◽  
Network Communities ◽  
Antlion Optimization

Antlion Optimization (ALO) is one of the latest population based optimization methods that proved its good performance in a variety of applications. The ALO algorithm copies the hunting mechanism of antlions to ants in nature. Community detection in social networks is conclusive to understanding the concepts of the networks. Identifying network communities can be viewed as a problem of clustering a set of nodes into communities. k-median clustering is one of the popular techniques that has been applied in clustering. The problem of clustering network can be formalized as an optimization problem where a qualitatively objective function that captures the intuition of a cluster as a set of nodes with better in ternal connectivity than external connectivity is selected to be optimized. 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. Experimental results which are applied on real life networks show the ability of the mixture antlion optimization and k-median to detect successfully an optimized community structure based on putting the modularity as an objective function.

Local community detection based on modularity metric G

2015 ◽  
Vol 29 (33) ◽  
pp. 1550215 ◽  
Author(s):  
Zhengyou Xia ◽  
Xiangying Gao ◽  
Xia Zhang
Keyword(s):  
Network Analysis ◽  
Community Detection ◽  
World Wide ◽  
Local Community ◽  
Complete Information ◽  
Detection Problem ◽  
Two Phase ◽  
The World ◽  
Local Community Detection

In complex network analysis, the local community detection problem is getting more and more attention. Because of the difficulty to get complete information of the network, such as the World Wide Web, the local community detection has been proposed by researcher. That is, we can detect a community from a certain source vertex with limited knowledge of an entire graph. The previous methods of local community detection now are more or less inadequate in some places. In this paper, we have proposed a new local modularity metric [Formula: see text] and based on it, a two-phase algorithm is proposed. The method we have taken is a greedy addition algorithm which means adding vertices into the community until [Formula: see text] does not increase. Compared with the previous methods, when our method is calculating the modularity metric, the range of vertices what we considered may affect the quality of the community detection wider. The results of experiments show that whether in computer-generated random graph or in the real networks, our method can effectively solve the problem of the local community detection.

scholarly journals A Neighborhood-Impact Based Community Detection Algorithm via Discrete PSO

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Dongqing Zhou ◽  
Xing Wang
Keyword(s):  
Community Detection ◽  
Real Life ◽  
Detection Algorithm ◽  
Label Propagation ◽  
Detection Problem ◽  
Signed Networks ◽  
Discrete Pso ◽  
Community Detection Algorithm ◽  
Definition Of ◽  
The Impact

The paper addresses particle swarm optimization (PSO) into community detection problem, and an algorithm based on new label strategy is proposed. In contrast with other label propagation strategies, the main contribution of this paper is to design the definition of the impact of node and take it into use. Special initialization and update approaches based on it are designed in order to make full use of it. Experiments on synthetic and real-life networks show the effectiveness of proposed strategy. Furthermore, this strategy is extended to signed networks, and the corresponding objective function which is called modularity density is modified to be used in signed networks. Experiments on real-life networks also demonstrate that it is an efficacious way to solve community detection problem.

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