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Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Clustering results on Dolphin club network by MOEA/DM.(a) The real structure of Dolphin network. (b) The prediction structure of Dolphin club network detected by MOEA/DM.
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f7: Clustering results on Dolphin club network by MOEA/DM.(a) The real structure of Dolphin network. (b) The prediction structure of Dolphin club network detected by MOEA/DM.

Mentions: In New Zealand’s life habits of 62 bottlenose dolphins, Lusseau30 found the dolphin’s interaction with a specific pattern, and constructed a social network containing 62 nodes. This dolphin network is naturally separated into two large groups: female and male. In Fig. 7(a), we show the true situation of the clustering dolphin network, In Fig. 7(b) we present the results of the clustering algorithm, MOEA/DM. In Fig. 7(b), MOEA/DM is divided into four categories: the top part is divided into two categories and the bottom part also divided into two parts.


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Clustering results on Dolphin club network by MOEA/DM.(a) The real structure of Dolphin network. (b) The prediction structure of Dolphin club network detected by MOEA/DM.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
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getmorefigures.php?uid=PMC5037381&req=5

f7: Clustering results on Dolphin club network by MOEA/DM.(a) The real structure of Dolphin network. (b) The prediction structure of Dolphin club network detected by MOEA/DM.
Mentions: In New Zealand’s life habits of 62 bottlenose dolphins, Lusseau30 found the dolphin’s interaction with a specific pattern, and constructed a social network containing 62 nodes. This dolphin network is naturally separated into two large groups: female and male. In Fig. 7(a), we show the true situation of the clustering dolphin network, In Fig. 7(b) we present the results of the clustering algorithm, MOEA/DM. In Fig. 7(b), MOEA/DM is divided into four categories: the top part is divided into two categories and the bottom part also divided into two parts.

View Article: PubMed Central - PubMed

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.

No MeSH data available.