s-core network decomposition: A generalization of k-core analysis to weighted networks
s-core network decomposition: A generalization of k-core analysis to weighted networks
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DOI：
10.1103/physreve.88.062819
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发表时间：
2013-12-30
影响因子：
2.4
通讯作者：
Almaas, Eivind
中科院分区：
文献类型：
--
作者：
Eidsaa, Marius;Almaas, Eivind
涵盖生物学、技术和社会现象的广泛系统可以用复杂网络来表示和分析。最近使用 k 核分解对此类网络的研究发现了发挥重要作用的节点组。在这里，我们提出 s 核分析，这是 k 核（或 k 壳）分析对复杂网络的推广，其中链接具有不同的强度或权重。我们在两个随机网络（ER 和具有无标度度分布的配置模型）上演示了 s 核分解方法，其中链接权重是 (i) 随机的，(ii) 相关的，以及 (iii) 与节点度反相关的。最后，我们在两个基因表达实验的背景下将 s-core 分解方法应用于酿酒酵母的蛋白质相互作用网络：对氢过氧化异丙苯 (CHP) 的氧化应激反应和发酵应激反应 (FSR)。我们发现最里面的 s 核心 (i) 与最里面的 k 核心不同，(ii) 对于两种应激条件 CHP 和 FSR 不同，(iii) 富含蛋白质，其生物学功能可以深入了解酵母如何管理这些特定应激。
A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.