2016
DOI: 10.1016/j.neucom.2016.06.030
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A Michigan memetic algorithm for solving the community detection problem in complex network

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Cited by 32 publications
(7 citation statements)
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“…In this experiment, the performance of the AntLP algorithm for finding the communities in term of the modularity Q is compared with other community detection algorithms including LPA [39], MLPA [17], CNM [42], GANET [43], CLA [44], MLAMA [45] and CLACD [28]. The results presented in Table 4, including both the maximum (Max) and average (Avg.)…”
Section: Simulation Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…In this experiment, the performance of the AntLP algorithm for finding the communities in term of the modularity Q is compared with other community detection algorithms including LPA [39], MLPA [17], CNM [42], GANET [43], CLA [44], MLAMA [45] and CLACD [28]. The results presented in Table 4, including both the maximum (Max) and average (Avg.)…”
Section: Simulation Resultsmentioning
confidence: 99%
“…The t ‐score to check if the two algorithm means are different can be calculated by the following equation: tscore=)(m1m2var1/n1+var2/n2 where mi , vari , ni are the means, variances, and the number of runs for algorithm i , respectively. The results of t ‐scores are presented in Table 5, where P denotes the performance of AntLP algorithm versus other community detection algorithms (LPA [39], MLPA [17], CNM [42], GANET [43], CLA [44], MLAMA [45] and CLACD [28]). The results of LPA, MLPA, CLA, and CLACD are extracted from our simulations of these algorithms and the results of CNM, GANET, and MLAMA directly are adopted from reports of their papers.…”
Section: Simulation Resultsmentioning
confidence: 99%
“…Many approaches have been proposed to solve the community detection problem in online social networks. MLAMA-Net [34] is an evolutionary algorithm, which solves the community detection problem in a network of chromosomes using evolutionary operators and local searches. In MLAMA-Net, each node includes a chromosome and a learning automaton.…”
Section: Community-based Algorithmmentioning
confidence: 99%
“…In our previous work, a Michigan MA, called MLAMA‐Net, was proposed for solving the community detection problem. The MLAMA‐Net algorithm is an EA in which each chromosome represents a part of the solution and the whole population represents the solution.…”
Section: Related Workmentioning
confidence: 99%
“…Learning automata have a vast variety of applications in combinatorial optimization problems, computer networks, queuing theory, image processing, information retrieval, adaptive control, neural network engineering, cloud computing, social networks, and pattern recognition …”
Section: Learning Automatamentioning
confidence: 99%