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.

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.

Nature inspired algorithms for solving the community detection problem

2017 ◽  
Vol 25 (6) ◽  
pp. 902-914
Author(s):  
Eslam Hussein ◽  
Ahmed Ibrahem Hafez ◽  
Aboul Ella Hassanien ◽  
Aly A Fahmy
Keyword(s):  
Community Detection ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Fitness Function ◽  
Bat Algorithm ◽  
Detection Problem ◽  
Artificial Fish Swarm Algorithm ◽  
Swarm Algorithms ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

Abstract Nature inspired Swarm algorithms have proven to be effective in solving recent complex optimization problems. Comparing such algorithm is a difficult task due to many facts, the nature of the swarm, the nature of the optimization problem itself and number of controlling parameters of the swarm algorithm. In this work we compared two recent swarm algorithms applied to the community detection problem which are the Bat Algorithm (BA) and Artificial Fish Swarm Algorithm (AFSA). Community detection is an active problem in social network analysis. The problem of detecting communities can be represented as an optimization problem where a quality fitness function that captures the intuition of a community as a group of nodes with better internal connectivity than external connectivity is chosen to be optimized. We also investigated the application of the BA and AFSA in solving the community section problem. And introduced a comparative analysis between the two algorithms and other well-known methods. The study show the effectiveness and the limitations of both algorithms.

scholarly journals A Novel Social Network Structural Balance Based on the Particle Swarm Optimization Algorithm

2015 ◽  
Vol 15 (2) ◽  
pp. 23-35 ◽  
Author(s):  
Li Zhao Xing ◽  
He Li Le ◽  
Zhang Hui
Keyword(s):  
Social Networks ◽  
Particle Swarm Optimization ◽  
Social Network ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Structural Balance

AbstractExploration of the structural balance of social networks is of great importance for theoretical analysis and practical use. This study modeled the structural balance of social networks as a mathematical optimization problem by using swarm intelligence, and an efficient discrete particle swarm optimization algorithm was proposed to solve the modeled optimization problem. To take advantage of the topologies of social networks in the algorithm design, the discrete representation of the particle was redefined, and the discrete particle update principles were redesigned. To validate the efficiency of the proposed algorithm, experiments were conducted using synthetic and real-world social networks. The experiments demonstrate that the proposed algorithm not only achieves a balanced social network structure, but also automatically detects the community topology of networks.

Ensemble of Community Detection in Social Networks Using Clustering of Users Demographic and Topological Information

2020 ◽  
Vol 01 ◽  
Author(s):  
Amin Rezaeipanah ◽  
Kambiz Ghanat
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Clustering Algorithms ◽  
Clustering Methods ◽  
Demographic Information ◽  
Swarm Optimization ◽  
Topological Information ◽  
Quantitative Evaluations ◽  
Better Than

Background: One of the great challenges in social network analysis is community detection. Community is a group of users which have high intra connections and sparse inter connections. Community detection or Clustering reveals community structure of social networks and hidden relationships among their constituents. Nowadays, many different methods are proposed to detect community structures in social networks from different perspective, but none of them can be a constant winner. Therefore, ensemble different methods can potentially improve the final result. Methods: In this paper, we present a framework for different methods to be combined for community detection. This method is a combination of genetic algorithms, particle swarm optimization, k-means clustering and Louvain clustering algorithms. Our method uses topological and demographic information to identify communities and can automatically determine the number of optimal communities. Results: Quantitative evaluations based on extensive experiments on Ego-Facebook social network dataset reveals that the method presented in this study achieves favorable results which are quite superior to other relevant algorithms in the literature.  Discovering relationships between individuals by analyzing social networks.  Providing identifying communities algorithms based on different clustering methods.  An ensemble of community detection consisting of GA, PSO, k-means and Louvain clustering.  The proposed method is better than the TSA method at silhouette and modularity criterion.  Demographic information also relates to the profile of users and their shared tweets.

scholarly journals A Novel Attributed Community Detection by Integration of Feature Weighting and Node Centrality

2021 ◽  
Author(s):  
Mehrdad Rostami ◽  
Mourad Oussalah
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Detection Method ◽  
State Of The Art ◽  
Feature Weighting ◽  
Label Propagation ◽  
Second Phase ◽  
Node Centrality ◽  
Propagation Algorithm

Abstract Community detection is one of the basic problems in social network analysis. Community detection on an attributed social networks aims to discover communities that have not only adhesive structure but also homogeneous node properties. Although community detection has been extensively studied, attributed community detection of large social networks with a large number of attributes remains a vital challenge. To address this challenge, a novel attributed community detection method through an integration of feature weighting with node centrality techniques is developed in this paper. The developed method includes two main phases: (1) Weight Matrix Calculation, (2) Label Propagation Algorithm-based Attributed Community Detection. The aim of the first phase is to calculate the weight between two linked nodes using structural and attribute similarities, while, in the second phase, an improved label propagation algorithm-based community detection method in attributed social network is proposed. The purpose of the second phase is to detect different communities by employing the calculated weight matrix and node popularity. After implementing the proposed method, its performance is compared with several other state of the art methods using some benchmarked real-world datasets. The results indicate that the developed method outperforms several other state of the art methods and ascertain the effectiveness of the developed method for attributed community detection.

scholarly journals A Label Propagation Based User Locations Prediction Algorithm in Social Network

2020 ◽  
pp. 165-174
Author(s):  
Huan Ma ◽  
Wei Wang
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Community Detection ◽  
Label Propagation ◽  
Prediction Algorithm ◽  
Location Prediction ◽  
Network Graph ◽  
Propagation Algorithm ◽  
Node Location ◽  
User Location

AbstractNetwork community detection is an important service provided by social networks, and social network user location can greatly improve the quality of community detection. Label propagation is one of the main methods to realize the user location prediction. The traditional label propagation algorithm has the problems including “location label countercurrent” and the update randomness of node location label, which seriously affects the accuracy of user location prediction. In this paper, a new location prediction algorithm for social networks based on improved label propagation algorithm is proposed. By computing the K-hop public neighbor of any two point in the social network graph, the nodes with the maximal similarity and their K-hopping neighbors are merged to constitute the initial label propagation set. The degree of nodes not in the initial set are calculated. The node location labels are updated asynchronously is adopted during the iterative process, and the node with the largest degree is selected to update the location label. The improvement proposed solves the “location label countercurrent” and reduces location label updating randomness. The experimental results show that the proposed algorithm improves the accuracy of position prediction and reduces the time cost compared with the traditional algorithms.

scholarly journals Discovering the maximum k-clique on social networks using bat optimization algorithm

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Akram Khodadadi ◽  
Shahram Saeidi
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Social Network ◽  
Coding Theory ◽  
Evaluation Criteria ◽  
Convergence Speed ◽  
Optimization Approach ◽  
Computational Results ◽  
Complete Subgraph ◽  
Np Complete

AbstractThe k-clique problem is identifying the largest complete subgraph of size k on a network, and it has many applications in Social Network Analysis (SNA), coding theory, geometry, etc. Due to the NP-Complete nature of the problem, the meta-heuristic approaches have raised the interest of the researchers and some algorithms are developed. In this paper, a new algorithm based on the Bat optimization approach is developed for finding the maximum k-clique on a social network to increase the convergence speed and evaluation criteria such as Precision, Recall, and F1-score. The proposed algorithm is simulated in Matlab® software over Dolphin social network and DIMACS dataset for k = 3, 4, 5. The computational results show that the convergence speed on the former dataset is increased in comparison with the Genetic Algorithm (GA) and Ant Colony Optimization (ACO) approaches. Besides, the evaluation criteria are also modified on the latter dataset and the F1-score is obtained as 100% for k = 5.

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.

An Efficient Chaotic Salp Swarm Optimization Approach Based on Ensemble Algorithm for Solving Class Imbalance Problem

2021 ◽  
Author(s):  
Rekha G ◽  
Krishna Reddy V ◽  
chandrashekar jatoth ◽  
Ugo Fiore
Keyword(s):  
Feature Selection ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Fitness Function ◽  
Class Imbalance ◽  
Optimization Approach ◽  
Class Imbalance Problem ◽  
Swarm Optimization ◽  
Adaboost Algorithm ◽  
Wide Range

Abstract Class imbalance problems have attracted the research community but a few works have focused on feature selection with imbalanced datasets. To handle class imbalance problems, we developed a novel fitness function for feature selection using the chaotic salp swarm optimization algorithm, an efficient meta-heuristic optimization algorithm that has been successfully used in a wide range of optimization problems. This paper proposes an Adaboost algorithm with chaotic salp swarm optimization. The most discriminating features are selected using salp swarm optimization and Adaboost classifiers are thereafter trained on the features selected. Experiments show the ability of the proposed technique to find the optimal features with performance maximization of Adaboost.

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