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

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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 Football club network by MOEA/DM.(a) The real structure of Football network. (b) The prediction structure of Football network detected by MOEA/DM. (c) The apart of (b).
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f9: Clustering results on Football club network by MOEA/DM.(a) The real structure of Football network. (b) The prediction structure of Football network detected by MOEA/DM. (c) The apart of (b).

Mentions: The network is divided into twelve categories as shown in Fig. 9(a). Figure 9(b) shows the classification results after using the MOEA/DM algorithm. A comparison of Fig. 9(a) with Fig. 9(b) shows that the football network has a more complex structure than the Dolphin and Karate networks. In the football network, nodes belong to the same classare relatively decentralized. The real network structure, shown in Fig. 9(a), and our predicted network structure, shown in Fig. 9(b), have the same number of categories. From Fig. 9(b) we extracted the three categories on the right and placed them in Fig. 9(c). The three categories in Fig. 9(c) appear to classify the wrong point. The point that marked 58, 29 and the 43, 37, 91 be divided into the wrong position. An analysis of these points reveals that a characteristic they have in common is connecting to other classes is more prevalent than connecting to the edges of their own classes. The MOPSO algorithm divides the network into a like category, but more than 10 points are incorrectly placed.


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

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

f9: Clustering results on Football club network by MOEA/DM.(a) The real structure of Football network. (b) The prediction structure of Football network detected by MOEA/DM. (c) The apart of (b).
Mentions: The network is divided into twelve categories as shown in Fig. 9(a). Figure 9(b) shows the classification results after using the MOEA/DM algorithm. A comparison of Fig. 9(a) with Fig. 9(b) shows that the football network has a more complex structure than the Dolphin and Karate networks. In the football network, nodes belong to the same classare relatively decentralized. The real network structure, shown in Fig. 9(a), and our predicted network structure, shown in Fig. 9(b), have the same number of categories. From Fig. 9(b) we extracted the three categories on the right and placed them in Fig. 9(c). The three categories in Fig. 9(c) appear to classify the wrong point. The point that marked 58, 29 and the 43, 37, 91 be divided into the wrong position. An analysis of these points reveals that a characteristic they have in common is connecting to other classes is more prevalent than connecting to the edges of their own classes. The MOPSO algorithm divides the network into a like category, but more than 10 points are incorrectly placed.

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.