期刊
PHYSICAL REVIEW E
卷 85, 期 2, 页码 -出版社
AMER PHYSICAL SOC
DOI: 10.1103/PhysRevE.85.026106
关键词
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资金
- Science Foundation Ireland [06/IN.1/I366, MACSI 06/MI/005, 09/SRC/E1780]
- INSPIRE: IRCSET-Marie Curie International Mobility Fellowship in Science Engineering and Technology
- James S. McDonnell Foundation [220020177]
- EPSRC [EP/I016058/1]
- NSF [DMS-0645369]
- EPSRC [EP/I016058/1] Funding Source: UKRI
- Engineering and Physical Sciences Research Council [EP/I016058/1] Funding Source: researchfish
Mean-field analysis is an important tool for understanding dynamics on complex networks. However, surprisingly little attention has been paid to the question of whether mean-field predictions are accurate, and this is particularly true for real-world networks with clustering and modular structure. In this paper, we compare mean-field predictions to numerical simulation results for dynamical processes running on 21 real-world networks and demonstrate that the accuracy of such theory depends not only on the mean degree of the networks but also on the mean first-neighbor degree. We show that mean-field theory can give (unexpectedly) accurate results for certain dynamics on disassortative real-world networks even when the mean degree is as low as 4.
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