期刊
PHYSICAL REVIEW X
卷 6, 期 1, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevX.6.011036
关键词
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资金
- James S. McDonnell Foundation
- Spanish Ministerio de Economia y Competitividad (MINECO) [FIS2013-47532-C3, FIS2015-71563-ERC]
- European Union [PIRG-GA-2010-277166, PIRG-GA-2010-268342]
- European Union FET Grant [317532]
- ICREA Funding Source: Custom
In complex systems, the network of interactions we observe between systems components is the aggregate of the interactions that occur through different mechanisms or layers. Recent studies reveal that the existence of multiple interaction layers can have a dramatic impact in the dynamical processes occurring on these systems. However, these studies assume that the interactions between systems components in each one of the layers are known, while typically for real-world systems we do not have that information. Here, we address the issue of uncovering the different interaction layers from aggregate data by introducing multilayer stochastic block models (SBMs), a generalization of single-layer SBMs that considers different mechanisms of layer aggregation. First, we find the complete probabilistic solution to the problem of finding the optimal multilayer SBM for a given aggregate-observed network. Because this solution is computationally intractable, we propose an approximation that enables us to verify that multilayer SBMs are more predictive of network structure in real-world complex systems.
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