title: An activation force-based affinity measure for analyzing complex networks.
abstract: Affinity measure is a key factor that determines the quality of the analysis of a complex network. Here, we introduce a type of statistics, activation forces, to weight the links of a complex network and thereby develop a desired affinity measure. We show that the approach is superior in facilitating the analysis through experiments on a large-scale word network and a protein-protein interaction (PPI) network consisting of ∼5,000 human proteins. The experiment on the word network verifies that the measured word affinities are highly consistent with human knowledge. Further, the experiment on the PPI network verifies the measure and presents a general method for the identification of functionally similar proteins based on PPIs. Most strikingly, we find an affinity network that compactly connects the cancer-associated proteins to each other, which may reveal novel information for cancer study; this includes likely protein interactions and key proteins in cancer-related signal transduction pathways.
authors.affiliation: School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, No.10 Xitucheng Road, Haidian District, Beijing, China. guojun@bupt.edu.cn
authors.forename: Jun
authors.initials: J
authors.lastname: Guo
authors.forename: Hanliang
authors.initials: H
authors.lastname: Guo
authors.forename: Zhanyi
authors.initials: Z
authors.lastname: Wang
mesh_terms: D001245:Association Learning; D019295:Computational Biology; D006801:Humans; D007802:Language; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000378:metabolism; D009369:Neoplasms / Q000378:metabolism; D009415:Nerve Net / Q000378:metabolism; D060066:Protein Interaction Maps; D015398:Signal Transduction
chemical_list: D009363:Neoplasm Proteins