s-core network decomposition: A generalization of k-core analysis to weighted networks

摘要

A broad range of systems spanning biology, technology, and social phenomena may be represented andnanalyzed as complex networks. Recent studies of such networks using k-core decomposition have uncoveredngroups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (ornk-shell) analysis to complex networks where the links have different strengths or weights. We demonstratenthe s-core decomposition approach on two random networks (ER and configuration model with scale-freendegree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the nodendegrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeastnSaccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response toncumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores aren(i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enrichednwith proteins whose biological functions give insight into how yeast manages these specific stresses.