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.

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.

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 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.

COMMUNITY DETECTION IN NETWORKS

2010 ◽  
Vol 20 (02) ◽  
pp. 361-367 ◽  
Author(s):  
C. O. DORSO ◽  
A. D. MEDUS
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
New Method

The problem of community detection is relevant in many disciplines of science. A community is usually defined, in a qualitative way, as a subset of nodes of a network which are more connected among themselves than to the rest of the network. In this article, we introduce a new method for community detection in complex networks. We define new merit factors based on the weak and strong community definitions formulated by Radicchi et al. [2004] and we show that this local definition properly describes the communities observed experimentally in two typical social networks.

scholarly journals Image segmentation with a multilevel threshold using backtracking search optimization algorithm

2020 ◽  
Vol 25 (2) ◽  
pp. 102
Author(s):  
Hather Ibraheem Abed
Keyword(s):  
Image Segmentation ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Threshold Method ◽  
Effective Technique ◽  
Backtracking Search ◽  
Search Optimization ◽  
Segmentation Methods ◽  
Backtracking Search Optimization Algorithm ◽  
Huge Challenge

Image segmentation is an important process in image processing. Though, there are many applications are affected by the segmentation methods and algorithms, unfortunately, not one technique, but the threshold is the popular one. Threshold technique can be categorized into two ways either simple threshold which has one threshold or multi- thresholds separated which has more than two thresholds . In this paper, image segmentation is used simple threshold method which is a simple and effective technique. Therefore, to calculate the value of threshold solution which is led to increase exponentially threshold that gives multi-thresholds image segmentation present a huge challenge. This paper is considered the multi-thresholds segmentation model for the optimization problem in order to overcome the problem of excessive calculation. The objective of this paper proposed an slgorithmto solve the optimization problem and realize multi-thresholds image segmentation. The proposed multi-thresholds segmentation algorithm should be segmented  the raw  image into pieces, and compared with other algorithms results. The experimental results that show multi-thresholds image segmentation based on backtracking search optimization algorithm are feasible and have a good segmentation.   http://dx.doi.org/10.25130/tjps.25.2020.036

scholarly journals Detecting Community Structures Within Complex Networks Using a Discrete Unconscious Search Algorithm

2021 ◽  
Vol 12 (2) ◽  
pp. 15-32
Author(s):  
Ehsan Ardjmand ◽  
William A. Young II ◽  
Najat E. Almasarwah
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Academic Community ◽  
Detection Methods ◽  
The Unconscious ◽  
Comprehensive Review ◽  
Wide Range

Detecting the communities that exist within complex social networks has a wide range of application in business, engineering, and sociopolitical settings. As a result, many community detection methods are being developed by researchers in the academic community. If the communities within social networks can be more accurately detected, the behavior or characteristics of each community within the networks can be better understood, which implies that better decisions can be made. In this paper, a discrete version of an unconscious search algorithm was applied to three widely explored complex networks. After these networks were formulated as optimization problems, the unconscious search algorithm was applied, and the results were compared against the results found from a comprehensive review of state-of-the-art community detection methods. The comparative study shows that the unconscious search algorithm consistently produced the highest modularity that was discovered through the comprehensive review of the literature.

scholarly journals Modularity based Community Detection in Social Networks

2020 ◽  
Vol 9 (3) ◽  
pp. 564-569
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Community Detection ◽  
Real World ◽  
Research Community ◽  
Incorrect Result ◽  
The Real

The community detection is an interesting and highly focused area in the analysis of complex networks (CNA). It identifies closely connected clusters of nodes. In recent years, several approaches have been proposed for community detection and validation of the result. Community detection approaches that use modularity as a measure have given much weight-age by the research community. Various modularity based community detection approaches are given for different domains. The network in the real-world may be weighted, heterogeneous or dynamic. So, it is inappropriate to apply the same algorithm for all types of networks because it may generate incorrect result. Here, literature in the area of community detection and the result evaluation has been extended with an aim to identify various shortcomings. We think that the contribution of facts given in this paper can be very useful for further research.

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