ArticleScientific reports2016
Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- A multi-objective scheduling optimization algorithm of a camera network for directional road network coverage.PloS one · 2018Article
- An enhanced artificial bee colony algorithm (EABC) for solving dispatching of hydro-thermal system (DHTS) problem.PloS one · 2018Article
- Integrative Pathway Analysis of Genes and Metabolites Reveals Metabolism Abnormal Subpathway Regions and Modules in Esophageal Squamous Cell Carcinoma.Molecules (Basel, Switzerland) · 2017Article
- An algorithm based on positive and negative links for community detection in signed networks.Scientific reports · 2017Article
- Optimal experimental conditions for Welan gum production by support vector regression and adaptive genetic algorithm.PloS one · 2017Article
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- A novel approach for predicting microbe-disease associations by bi-random walk on the heterogeneous network.PloS one · 2017Article
- Optimization to the Phellinus experimental environment based on classification forecasting method.PloS one · 2017Article
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- A functional pipeline framework for landmark identification on 3D surface extracted from volumetric data.PloS one · 2017Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The field of complex network clustering is gaining considerable attention in recent years. In this study, a multi-objective evolutionary algorithm based on membranes is proposed to solve the network clustering problem. Population are divided into different membrane structures on average. The evolutionary algorithm is carried out in the membrane structures. The population are eliminated by the vector of membranes. In the proposed method, two evaluation objectives termed as Kernel J-means and Ratio Cut are to be minimized. Extensive experimental studies comparison with state-of-the-art algorithms proves that the proposed algorithm is effective and promising.
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Registered trials
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