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

Pizzuti, Clara

doi:10.1007/978-3-540-87700-4_107

Cited by 376 publications

(246 citation statements)

References 17 publications

Supporting

Mentioning

231

Contrasting

Order By: Relevance

“…We can we find that when there is higher similarity between nodes (nodes are initialized as populations in this paper), the fitness value is larger [22]. When designing CD fitness values, the simple strategy using the Euclidean distance or Jaccard similarity is not enough [43,44]. It should be noted that similarity of network nodes is closely related to factors [45] such as number of common neighbor nodes, local influence of nodes, direct or indirect connected edge, topic correlation network, etc.…”

Section: Fitness Function Of Community Detectionmentioning

confidence: 91%

See 1 more Smart Citation

Community Detection Based on Differential Evolution Using Social Spider Optimization

Wang

Xue-jun

et al. 2017

Symmetry

View full text Add to dashboard Cite

Community detection (CD) has become an important research direction for data mining in complex networks. Evolutionary algorithm-based (EA-based) approaches, among many other existing community detection methods, are widely used. However, EA-based approaches are prone to population degradation and local convergence. Developing more efficient evolutionary algorithms thus becomes necessary. In 2013, Cuevas et al. proposed a new differential evolution (DE) hybrid meta-heuristic algorithm based on the simulated cooperative behavior of spiders, known as social spider optimization (SSO). On the basis of improving the SSO algorithm, this paper proposes a community detection algorithm based on differential evolution using social spider optimization (DESSO/CD). In this algorithm, the CD detection process is done by simulating the spider cooperative operators, marriage, and operator selection. The similarity of nodes is defined as local fitness function; the community quality increment is used as a screening criterion for evolutionary operators. Populations are sorted according to their contribution and diversity, making evolution even more different. In the entire process, a random cloud crossover model strategy is used to maintain population diversity. Each generation of the mating radius of the SSO algorithm will be adjusted appropriately according to the iterative times and fitness values. This strategy not only ensures the search space of operators, but also reduces the blindness of exploration. On the other hand, the multi-level, multi-granularity strategy of DESSO/CD can be used to further compensate for resolution limitations and extreme degradation defects based on modular optimization methods. The experimental results demonstrate that the DESSO/CD method could detect the community structure with higher partition accuracy and lower computational cost when compared with existing methods. Since the application of the SSO algorithm in CD research is just beginning, the study is competitive and promising.

show abstract

Section: Fitness Function Of Community Detectionmentioning

confidence: 91%

“…One is the standard for testing quality, and the other is used as the objective function [43]. Although Q functions are limited in resolution [28] and extreme degradation [31], they are widely used by researchers.…”

Section: Community Optimization Incremental Criteriamentioning

confidence: 99%

Community Detection Based on Differential Evolution Using Social Spider Optimization

Wang

Xue-jun

et al. 2017

Symmetry

View full text Add to dashboard Cite

show abstract

“…In [9], particle swarm optimization was applied to detect community structures, and experimental results showed that the isolated nodes can be detected with increased ease. A genetic algorithm (GA) developed in [31] to solve the community detection problem with a single-objective function based on community score; the quality of the detected community structures was better than that of the previous GA approaches. A GA approach was also proposed in [15] to solve the community structure detection problem with a multi-objective function, improving the single-objective function designed in [31].…”

Section: Detection Of Community Structuresmentioning

confidence: 99%

“…A genetic algorithm (GA) developed in [31] to solve the community detection problem with a single-objective function based on community score; the quality of the detected community structures was better than that of the previous GA approaches. A GA approach was also proposed in [15] to solve the community structure detection problem with a multi-objective function, improving the single-objective function designed in [31]. The k-means algorithm was first adopted by [22] to find an initial solution, which considered link length between nodes in a network, and then applied the simulated annealing algorithm to detect community structures.…”

Section: Detection Of Community Structuresmentioning

confidence: 99%

An integer programming approach and visual analysis for detecting hierarchical community structures in social networks

Lin

Kang

Chen

2015

Information Sciences

View full text Add to dashboard Cite

“…Especially in recent years, the multi-objective evolutionary algorithm must do optimization of the more widely used and studied ones, resulting in a series of novel algorithms and get good application. The multiobjective optimization proposition is generally no unique global optimal solution, so this is actually a multi-objective optimization proposition of the process of seeking a Pareto set [7]. The traditional multi-objective algorithm is often converted into a single objective proposition after the use of a sophisticated single-objective optimization algorithm.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%