A Discrete Black Hole Optimization Algorithm for Efficient Community Detection in Social Networks

2018 ◽  
pp. 133-153
Author(s):  
Mohamed Guendouz
Keyword(s):  
Social Networks ◽  
Black Hole ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Np Hard Problem ◽  
Density Evaluation ◽  
Evaluation Measure ◽  
Networks Analysis ◽  
Modularity Density ◽  
New Strategies

In recent years, social networks analysis has attracted the attention of many researchers. Community detection is one of the highly studied problems in this field. It is considered an NP-hard problem, and several algorithms have been proposed to solve this problem. In this chapter, the authors present a new algorithm for community detection in social networks based on the Black Hole optimization algorithm. The authors use the modularity density evaluation measure as a function to maximize. They also propose the enhancement of the algorithm by using two new strategies: initialization and evolution. The proposed algorithm has been tested on famous synthetic and real-world networks; experimental results compared with three known algorithms show the effectiveness of using this algorithm for community detection in social networks.

Penguins Search Optimization Algorithm for Community Detection in Complex Networks

2018 ◽  
Vol 9 (1) ◽  
pp. 1-14 ◽  
Author(s):  
Mohamed Guendouz ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Search Optimization ◽  
Density Evaluation ◽  
Evaluation Measure ◽  
Modularity Density ◽  
Initialization Strategy

In the last decade, the problem of community detection in complex networks has attracted the attention of many researchers in many domains, several methods and algorithms have been proposed to deal with this problem, many of them consider it as an optimization problem and various bio-inspired algorithms have been applied to solve it. In this work, the authors propose a new method for community detection in complex networks using the Penguins Search Optimization Algorithm (PeSOA), the authors use the modularity density evaluation measure as a function to maximize and they propose also to enhance the algorithm by using a new initialization strategy. The proposed algorithm has been tested on four popular real-world networks; experimental results compared with other known algorithms show the effectiveness of using this method for community detection in social networks.

Community Detection Using Fireworks Optimization Algorithm

2019 ◽  
Vol 28 (03) ◽  
pp. 1950010 ◽  
Author(s):  
Imane Messaoudi ◽  
Nadjet Kamel
Keyword(s):  
Social Networks ◽  
Mutual Information ◽  
Objective Function ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Complex Structure ◽  
Initial Population ◽  
Fireworks Algorithm ◽  
Normalized Mutual Information ◽  
Modularity Density

Since community detection is an important tool for understanding the complex structure of social networks, an improved fireworks algorithm is proposed in this paper. The algorithm generates the initial population with the Affinity Propagation approach to have high initialization quality. The algorithm optimizes the modularity density as objective function by calculating the amplitude, the number of sparks and exploring the sparks. One firework is mutated twice, randomly and according to the label of its neighbors. Experiments on both real and synthetic networks show that the proposed algorithm achieves more accurate results in terms of modularity and normalized mutual information.

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.

A Nature-Inspired Metaheuristic Cuckoo Search Algorithm for Community Detection in Social Networks

2017 ◽  
Vol 8 (1) ◽  
pp. 50-62 ◽  
Author(s):  
Ramadan Babers ◽  
Aboul Ella Hassanien
Keyword(s):  
Social Networks ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Search Algorithm ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Krill Herd ◽  
Artificial Fish Swarm ◽  
Lion Optimization Algorithm

In last few years many approaches have been proposed to detect communities in social networks using diverse ways. Community detection is one of the important researches in social networks and graph analysis. This paper presents a cuckoo search optimization algorithm with Lévy flight for community detection in social networks. Experimental on well-known benchmark data sets demonstrates that the proposed algorithm can define the structure and detect communities of complex networks with high accuracy and quality. In addition, the proposed algorithm is compared with some swarms algorithms including discrete bat algorithm, artificial fish swarm, discrete Krill Herd, ant lion algorithm and lion optimization algorithm and the results show that the proposed algorithm is competitive with these algorithms.

An Efficient Approach for Community Detection in Complex Social Networks Based on Elephant Swarm Optimization Algorithm

2017 ◽  
pp. 1062-1075 ◽  
Author(s):  
Khaled Ahmed ◽  
Aboul Ella Hassanien ◽  
Ehab Ezzat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Optimization Algorithm ◽  
Label Propagation ◽  
Optimization Approach ◽  
Detection Problem ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Complex social networks analysis is an important research trend, which basically based on community detection. Community detection is the process of dividing the complex social network into a dynamic number of clusters based on their edges connectivity. This paper presents an efficient Elephant Swarm Optimization Algorithm for community detection problem (EESO) as an optimization approach. EESO can define dynamically the number of communities within complex social network. Experimental results are proved that EESO can handle the community detection problem and define the structure of complex networks with high accuracy and quality measures of NMI and modularity over four popular benchmarks such as Zachary Karate Club, Bottlenose Dolphin, American college football and Facebook. EESO presents high promised results against eight community detection algorithms such as discrete krill herd algorithm, discrete Bat algorithm, artificial fish swarm algorithm, fast greedy, label propagation, walktrap, Multilevel and InfoMap.

scholarly journals Community Detection Problem Based on Polarization Measures. An application to Twitter: the COVID-19 case in Spain

2021 ◽  
Author(s):  
Inmaculada Gutiérrez García-Pardo ◽  
Juan Antonio Guevara Gil ◽  
Daniel Gómez González ◽  
Javier Castro Cantalejo ◽  
Rosa Espínola Vílchez
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 possibility 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 probably 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.

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