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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.


Prediction structure of SFI detected by MOEA/DM.
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f11: Prediction structure of SFI detected by MOEA/DM.

Mentions: The SFI34 network represents 271 scientists in residence at the Santa Fe Institute, Santa Fe, NM, USA, during any part of calendar year 1999 or 2000, and their collaborators. An edge is drawn between a pair of scientists if they coauthored one or more articles during the same time period. The biggest component of the SFI graph consists of 118 vertices and we only do experiments on this part. Figure 11 show the result of the MOEA/DM. From the picture, the network be divided into twelve category and MOPSO divided into eight category. From the Table 4, the Q value result from MOEA/DM better than the result from MOPSO and the time cost less than MOPSO.


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Prediction structure of SFI detected by MOEA/DM.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f11: Prediction structure of SFI detected by MOEA/DM.
Mentions: The SFI34 network represents 271 scientists in residence at the Santa Fe Institute, Santa Fe, NM, USA, during any part of calendar year 1999 or 2000, and their collaborators. An edge is drawn between a pair of scientists if they coauthored one or more articles during the same time period. The biggest component of the SFI graph consists of 118 vertices and we only do experiments on this part. Figure 11 show the result of the MOEA/DM. From the picture, the network be divided into twelve category and MOPSO divided into eight category. From the Table 4, the Q value result from MOEA/DM better than the result from MOPSO and the time cost less than MOPSO.

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.