Overlapping community detection in complex networks using multi-objective evolutionary algorithm

Zhao, Yiru; Li, Shenghong; Feng, Jian

doi:10.1007/s40314-015-0260-1

Cited by 21 publications

(7 citation statements)

References 44 publications

(59 reference statements)

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“…Depending on the number of objectives, Evo-FSA approaches are split into single-objective and multi-objective approaches. The one that combines the number of features and the classification output of a single fitness function is known as single-objective FSA (23). Many of the current feature selection approaches seek to optimize the classification output either during the initial search or aggregate the classification results and attributes into a single objective attribute.…”

Section: Objective Numbermentioning

confidence: 99%

Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

Adi

Junaedi

Frismanda

et al. 2021

emitter

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Initially, the goal of Machine Learning (ML) advancements is faster computation time and lower computation resources, while the curse of dimensionality burdens both computation time and resource. This paper describes the benefits of the Feature Selection Algorithms (FSA) for speech data under workload stress. FSA contributes to reducing both data dimension and computation time and simultaneously retains the speech information. We chose to use the robust Evolutionary Algorithm, Harmony Search, Principal Component Analysis, Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, and Bee Colony Optimization, which are then to be evaluated using the hierarchical machine learning models. These FSAs are explored with the conversational workload stress data of a Customer Service hotline, which has daily complaints that trigger stress in speaking. Furthermore, we employed precisely 223 acoustic-based features. Using Random Forest, our evaluation result showed computation time had improved 3.6 faster than the original 223 features employed. Evaluation using Support Vector Machine beat the record with 0.001 seconds of computation time.

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Section: Objective Numbermentioning

confidence: 99%

Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

Adi

Junaedi

Frismanda

et al. 2021

emitter

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“…Numerous articles in the literature have done the tackling of the community detection Algorithm from the perspective of multi-objective optimization. Many researchers have assumed convexity of the problem and solve it using the sum of objectives such as the work of [16] and the work of [17], while less of them have separated the objectives and handled the problem using Pareto concept. Some researchers have formulated the problem using both inter and intra distance and found a set of solutions that balances between them with non-domination aspect [18].…”

Section: Related Workmentioning

confidence: 99%

“…While some researchers have focused on the community structure and its influence on the resilience of the network as a whole [19], many researchers have focused on the community detection from the perspective of multi-objective optimization. In the work of [16], the multi-objective evolutionary Algorithm has been used for identifying overlapping community structure in a complex network. This work has ignored the dynamic aspect of the network and its temporal changes.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective NSGA-II based community detection using dynamical evolution social network

Tharwat

Fudzee

Kasim

et al. 2021

IJECE

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Community detection is becoming a highly demanded topic in social networking-based applications. It involves finding the maximum intraconnected and minimum inter-connected sub-graphs in given social networks. Many approaches have been developed for community’s detection and less of them have focused on the dynamical aspect of the social network. The decision of the community has to consider the pattern of changes in the social network and to be smooth enough. This is to enable smooth operation for other community detection dependent application. Unlike dynamical community detection Algorithms, this article presents a non-dominated aware searching Algorithm designated as non-dominated sorting based community detection with dynamical awareness (NDS-CD-DA). The Algorithm uses a non-dominated sorting genetic algorithm NSGA-II with two objectives: modularity and normalized mutual information (NMI). Experimental results on synthetic networks and real-world social network datasets have been compared with classical genetic with a single objective and has been shown to provide superiority in terms of the domination as well as the convergence. NDS-CD-DA has accomplished a domination percentage of 100% over dynamic evolutionary community searching DECS for almost all iterations.

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“…For example, the precise information delivery, e.g., Google AdWords [ 1 ] increases the transaction amounts for sending the advertisement information to the right person. Therefore, detecting communities is a popular research topic [ 2 , 3 , 4 , 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…However, the above results do not deal with the overlapping properties. The overlapping networks have various properties, so some approaches consider the multi-objective approach to find the balanced results [ 4 , 5 , 6 , 31 ]. The balanced results mean that most properties are considered, but the derived results may not be closed to the real-world properties.…”

Section: Introductionmentioning

confidence: 99%

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

Tsung

Chen

et al. 2020

Entropy

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On the purpose of detecting communities, many algorithms have been proposed for the disjointed community sets. The major challenge of detecting communities from the real-world problems is to determine the overlapped communities. The overlapped vertices belong to some communities, so it is difficult to be detected using the modularity maximization approach. The major problem is that the overlapping structure barely be found by maximizing the fuzzy modularity function. In this paper, we firstly introduce a node weight allocation problem to formulate the overlapping property in the community detection. We propose an extension of modularity, which is a better measure for overlapping communities based on reweighting nodes, to design the proposed algorithm. We use the genetic algorithm for solving the node weight allocation problem and detecting the overlapping communities. To fit the properties of various instances, we introduce three refinement strategies to increase the solution quality. In the experiments, the proposed method is applied on both synthetic and real networks, and the results show that the proposed solution can detect the nontrivial valuable overlapping nodes which might be ignored by other algorithms.

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Overlapping community detection in complex networks using multi-objective evolutionary algorithm

Cited by 21 publications

References 44 publications

Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

Exploring the Time-efficient Evolutionary-based Feature Selection Algorithms for Speech Data under Stressful Work Condition

Multi-objective NSGA-II based community detection using dynamical evolution social network

Detecting Overlapping Communities in Modularity Optimization by Reweighting Vertices

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