论文标题
超越成对网络的相似性：探索网络之间的调解和抑制
Beyond pairwise network similarity: exploring Mediation and Suppression between networks
论文作者
论文摘要
网络相似性量化了两个网络对称性相关的方式和何时，包括统计关联的度量，例如成对距离或网络之间的其他相关度量或多路复用网络的层之间的其他相关度量，但是当这种相关性被CAUSAL相关性掩盖时，它们是否可以直接揭示是否存在隐藏的混淆网络因素。在这项工作中，我们将此成对的概念框架扩展到网络的三联体，并量化网络如何以及何时直接与第二个网络相关，或者通过间接调解或与第三网络的交互。因此，我们开发了一种简单，直观的设定理论方法来量化网络之间的调解和抑制。我们通过合成模型来验证我们的理论，并将其进一步应用于现实世界网络的三联体，揭示了调解和抑制效应，这些效应在考虑在线社交网络中的不同互动方式以及大脑中不同信息处理途径时会出现。
Network similarity measures quantify how and when two networks are symmetrically related, including measures of statistical association such as pairwise distance or other correlation measures between networks or between the layers of a multiplex network, but neither can directly unveil whether there are hidden confounding network factors nor can they estimate when such correlation is underpinned by a causal relation. In this work we extend this pairwise conceptual framework to triplets of networks and quantify how and when a network is related to a second network directly or via the indirect mediation or interaction with a third network. Accordingly, we develop a simple and intuitive set-theoretic approach to quantify mediation and suppression between networks. We validate our theory with synthetic models and further apply it to triplets of real-world networks, unveiling mediation and suppression effects which emerge when considering different modes of interaction in online social networks and different routes of information processing in the brain.
