Identification of important nodes in artificial bio-molecular networks

Identification of important nodes is an emerging hot topic in complex networks over the last few decades. The so-called important nodes are hub, influential nodes, leaders, and so on. To characterize the importance of nodes, various indexes are introduced in complex networks, such as degree, closeness, betweenness, k-shell, and principal component analysis based on the adjacency matrix. By using the above indexes and multivariate statistical analysis technique, this paper aims at developing a new approach to identify the important nodes in artificial bio-molecular networks generated from the duplication-divergence (DD) model. In particular, the statistical characteristics of important nodes are also investigated. The above results shed light on the potential real-world applications in bio-molecular networks, such as deducing the genes related to the specific disease.

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