超越成对网络相似性：探索网络之间的中介和抑制
Beyond pairwise network similarity: exploring Mediation and Suppression between networks
摘要：
网络相似性度量量化了两个网络如何以及何时对称相关，包括统计关联的度量，例如网络之间或多重网络层之间的成对距离或其他相关性度量，但既不能直接揭示是否存在隐藏的混淆网络因素，也不能估计这种相关性何时由因果关系支撑。在这项工作中，我们将这种成对的概念框架扩展到三元组网络，并量化一个网络如何以及何时与第二个网络直接或通过间接中介或与第三个网络的交互相关。因此，我们开发了一种简单直观的集合论方法来量化网络之间的调解和抑制。我们用合成模型验证了我们的理论，并进一步将其应用于现实世界网络的三元组，揭示了在考虑在线社交网络中的不同互动模式和大脑中信息处理的不同路径时出现的中介和抑制效应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
