Limits...
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.


Encode of network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Encode of network.

Mentions: Proposed in the graph structure is a genetics-based adjacency matrix notation29, where each individual, g, of the population consists of N genes, each of which, takes allele values, j, in the range 1, 2, …, N. Genes and alleles represent nodes of the graph G = (V, E) modeling a network. Thus, a value of j, assigned to the ith gene, is then interpreted as a link between the nodes i and j, and, in the resulting clustering solution, the nodes are in the same cluster. The decoding of this representation requires the identification of all connected components. All nodes belonging to the same connected component are then assigned to one cluster. A main advantage of this representation is that it is unnecessary to fix the number of clusters in advance, because the number of clusters is automatically determined in the decoding step. Figure 3 illustrates the locus-based adjacency scheme for a network of seven nodes.


Complex Network Clustering by a Multi-objective Evolutionary Algorithm Based on Decomposition and Membrane Structure
Encode of network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f3: Encode of network.
Mentions: Proposed in the graph structure is a genetics-based adjacency matrix notation29, where each individual, g, of the population consists of N genes, each of which, takes allele values, j, in the range 1, 2, …, N. Genes and alleles represent nodes of the graph G = (V, E) modeling a network. Thus, a value of j, assigned to the ith gene, is then interpreted as a link between the nodes i and j, and, in the resulting clustering solution, the nodes are in the same cluster. The decoding of this representation requires the identification of all connected components. All nodes belonging to the same connected component are then assigned to one cluster. A main advantage of this representation is that it is unnecessary to fix the number of clusters in advance, because the number of clusters is automatically determined in the decoding step. Figure 3 illustrates the locus-based adjacency scheme for a network of seven nodes.

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.