BACKGROUND:The notion of centrality is used to identify "important" nodes in social networks. Importance of nodes is not well-defined, and many different notions exist in the literature. The challenge of defining centrality in meaningful ways when network edges can be positively or negatively weighted has not been adequately addressed in the literature. Existing centrality algorithms also have a second shortcoming, i.e., the list of the most central nodes are often clustered in a specific region of the network and are not well represented across the network. METHODS:We address both by proposing Ablatio Triadum (ATria), an iterative centrality algorithm that uses the concept of "payoffs" from economic theory. RESULTS:We compare our algorithm with other known centrality algorithms and demonstrate how ATria overcomes several of their shortcomings. We demonstrate the applicability of our algorithm to synthetic networks as well as biological networks including bacterial co-occurrence networks, sometimes referred to as microbial social networks. CONCLUSIONS:We show evidence that ATria identifies three different kinds of "important" nodes in microbial social networks with different potential roles in the community.

译文

背景:中心性概念用于识别社交网络中的“重要”节点。节点的重要性尚未明确定义,并且文献中存在许多不同的概念。当网络边缘可以被正负加权时,以有意义的方式定义中心性的挑战尚未在文献中得到充分解决。现有的中心性算法还具有第二个缺点,即,最中心节点的列表通常被聚集在网络的特定区域中,并且不能在整个网络中很好地表示。
方法:我们通过提出Ablatio Triadum(ATria)来解决这两个问题,这是一种迭代的中心性算法,它使用了经济理论中的“收益”概念。
结果:我们将我们的算法与其他已知的中心性算法进行了比较,并演示了ATria如何克服其一些缺点。我们证明了我们的算法对合成网络以及包括细菌共生网络(有时称为微生物社交网络)在内的生物网络的适用性。
结论:我们显示出证据表明,ATria识别了微生物社会网络中三种不同类型的“重要”节点,这些节点在社区中具有不同的潜在作用。

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