RT Journal Article SR Electronic T1 Revisiting the General Concept of Network Centralities: A Propose for Centrality Analysis in Network Science JF bioRxiv FD Cold Spring Harbor Laboratory SP 149492 DO 10.1101/149492 A1 Minoo Ashtiani A1 Mehdi Mirzaie A1 Zahra Razaghi-Moghadam A1 Holger Hennig A1 Olaf Wolkenhauer A1 Ali Salehzadeh-Yazdi A1 Mohieddin Jafari YR 2017 UL http://biorxiv.org/content/early/2017/06/13/149492.abstract AB Background In network science, although different types of centrality measures have been introduced to determine important nodes of networks, a consensus pipeline to select and implement the best tailored measure for each complex network is still an open field. In the present study, we examine the node centrality profiles of protein-protein interaction networks (PPINs) in order to detect which measure is succeeding to predict influential proteins. We study and demonstrate the effects of inherent topological features and network reconstruction approaches on the centrality measure values.Results PPINs were used to compare a large number of well-known centrality measures. Unsupervised machine learning approaches, including principal component analysis (PCA) and clustering methods, were applied to find out how these measures are similar in terms of characterizing and assorting network influential constituents. We found that the principle components of the network centralities and the contribution level of them demonstrated a network-dependent significancy of these measures. We show that some centralities namely Latora, Decay, Lin, Freeman, Diffusion, Residual and Average had a high level of information in comparison with other measures in all PPINs. Finally, using clustering analysis, we restated that the determination of important nodes within a network depends on its topology.Conclusions Using PCA and identifying the contribution proportion of the variables, i.e., centrality measures in principal components, is a prerequisite step of network analysis in order to infer any functional consequences, e.g., essentiality of a node. Our conclusion is based on the signal and noise modeling using PCA and the similarity distance between clusters. Also, an interesting strong correlation between silhouette criterion and contribution value was found which corroborates our results.