GA-Net: A Genetic Algorithm for Community Detection in Social Networks

2008 ◽  
pp. 1081-1090 ◽  
Author(s):  
Clara Pizzuti
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Detection

Genetic algorithm-based community detection in large-scale social networks

2019 ◽  
Vol 32 (13) ◽  
pp. 9649-9665 ◽  
Author(s):  
Ranjan Kumar Behera ◽  
Debadatta Naik ◽  
Santanu Kumar Rath ◽  
Ramesh Dharavath
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Community Detection ◽  
Large Scale

scholarly journals COMPARATIVE ANALYSIS OF QUALITY METRICS FOR COMMUNITY DETECTION IN SOCIAL NETWORKS USING GENETIC ALGORITHM

2016 ◽  
Vol 26 (6) ◽  
pp. 625-641 ◽  
Author(s):  
Simrat Kaur ◽  
Sarbjeet Singh ◽  
Sakshi Kaushal ◽  
Arun Kumar Sangaiah
Keyword(s):  
Genetic Algorithm ◽  
Social Networks ◽  
Comparative Analysis ◽  
Community Detection ◽  
Quality Metrics

scholarly journals Optimization Algorithms for Detection of Social Interactions

Algorithms ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 139 ◽  
Author(s):  
Vincenzo Cutello ◽  
Georgia Fargetta ◽  
Mario Pavone ◽  
Rocco A. Scollo
Keyword(s):  
Social Networks ◽  
Local Search ◽  
Social Interactions ◽  
Community Detection ◽  
Experimental Analysis ◽  
Search Strategy ◽  
Random Search ◽  
Optimization Methods ◽  
Research Areas ◽  
Greedy Optimization

Community detection is one of the most challenging and interesting problems in many research areas. Being able to detect highly linked communities in a network can lead to many benefits, such as understanding relationships between entities or interactions between biological genes, for instance. Two different immunological algorithms have been designed for this problem, called Opt-IA and Hybrid-IA, respectively. The main difference between the two algorithms is the search strategy and related immunological operators developed: the first carries out a random search together with purely stochastic operators; the last one is instead based on a deterministic Local Search that tries to refine and improve the current solutions discovered. The robustness of Opt-IA and Hybrid-IA has been assessed on several real social networks. These same networks have also been considered for comparing both algorithms with other seven different metaheuristics and the well-known greedy optimization Louvain algorithm. The experimental analysis conducted proves that Opt-IA and Hybrid-IA are reliable optimization methods for community detection, outperforming all compared 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.

Sign in / Sign up

Export Citation Format

Share Document