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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 Karate club network by MOEA/DM.(a) The real structure of Karate network. (b) The prediction structure of Karate network detected by MOEA/DM.
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f6: Clustering results on Karate club network by MOEA/DM.(a) The real structure of Karate network. (b) The prediction structure of Karate network detected by MOEA/DM.

Mentions: In Fig. 6(a), we show the true situation of the clustering karate network, In Fig. 6(b) we present the results of the clustering algorithm, MOEA/DM. In Fig. 6(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. In Fig. 6(a), Point 10 (red) belongs to the real structure (upper part). According to our prediction, Point 10 (blue) should belong in the predicted structure (lower part) shown in Fig. 6(b). Other papers designate points (such as Point 10) as fussy nodes, i.e., it can be either classified to the first cluster or to the second one.


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

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

f6: Clustering results on Karate club network by MOEA/DM.(a) The real structure of Karate network. (b) The prediction structure of Karate network detected by MOEA/DM.
Mentions: In Fig. 6(a), we show the true situation of the clustering karate network, In Fig. 6(b) we present the results of the clustering algorithm, MOEA/DM. In Fig. 6(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. In Fig. 6(a), Point 10 (red) belongs to the real structure (upper part). According to our prediction, Point 10 (blue) should belong in the predicted structure (lower part) shown in Fig. 6(b). Other papers designate points (such as Point 10) as fussy nodes, i.e., it can be either classified to the first cluster or to the second one.

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.