The Influence of Three Statistical Variables on Self-Similarity in Complex Networks

The reason for the self-similarity property of complex network is still an open issue. In this paper, we focus on the influence of degree, betweenness, and coreness on self-similarity of complex network. Some nodes are removed from the original network based on the definitions of degree, betweenness, and coreness in the ascending and descending order. And then, some new networks are obtained after removing nodes. The self-similarities of original network and new networks are compared. Moreover, two real networks are used for numerical simulation, including a USAir network and the yeast protein interaction (YPI) network. The effects of the three statistical variables on the two real networks are considered. The results reveal that the nodes with large degree and betweenness have great effects on self-similarity, and the influence of coreness on self-similarity is small.

Identification of Important Proteins in Protein Interaction Network Based on SVM

Importance of proteins are different to perform functions of cells in living organisms according to the relevant experiment results, and more essential proteins is the most important kind of proteins. There are recently many computational approaches proposed to predict essential proteins in network level through network topologies combined with biological information of proteins. However it is still hard to identify them because of limitations of topological centralities and bioinformatic sources. And more it is the challenge is to perform better with less resources. Therefore in this paper, we first examine the correlation between common topological centralities and essential proteins and choose a few particular centralities, and then to build a SVM model, names as TC-SVM, for predicting the essential proteins. The new method has been applied to a yeast protein interaction networks, which are obtained from the BioGRID database. The ten folds experimental results show that the performance of predicting essential proteins by TC-SVM is excellent.