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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 Polbooks club network by MOEA/DM.(a) The real structure of American political book network. (b) The structure of American political book network detected by MOEA/DM.
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f10: Clustering results on Polbooks club network by MOEA/DM.(a) The real structure of American political book network. (b) The structure of American political book network detected by MOEA/DM.

Mentions: The network is divided into three categories as shown in Fig. 10(a). Figure 10(b) shows the classification results after using the MOEA/DM algorithm. A comparison of Fig. 10(a) with Fig. 10(b) shows that the political network has a more complex structure than the Football networks. In the political network, nodes belong to the same classare relatively decentralized. MOEA/DM divides the network into eight category and the part that the color mark red, have divides 9 points are incorrectly placed. The category that color marks blue, have divides four major sub category the color mark orange, green, pink and yellow.


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

License
Show All Figures
getmorefigures.php?uid=PMC5037381&req=5

f10: Clustering results on Polbooks club network by MOEA/DM.(a) The real structure of American political book network. (b) The structure of American political book network detected by MOEA/DM.
Mentions: The network is divided into three categories as shown in Fig. 10(a). Figure 10(b) shows the classification results after using the MOEA/DM algorithm. A comparison of Fig. 10(a) with Fig. 10(b) shows that the political network has a more complex structure than the Football networks. In the political network, nodes belong to the same classare relatively decentralized. MOEA/DM divides the network into eight category and the part that the color mark red, have divides 9 points are incorrectly placed. The category that color marks blue, have divides four major sub category the color mark orange, green, pink and yellow.

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.