Accuracy of mean-field theory for dynamics on real-world networks.

Journal article

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.

Authors: Gleeson, J; Melnik, S; Ward, J; Porter, M; Mucha, P

• Publication status: Published
• Journal: Physical review. E, Statistical, nonlinear, and soft matter physics
• Volume: 85
• Issue: 2 Pt 2
• Pages: 026106
• Publication date: 2012-02-01
• DOI: 10.1103/physreve.85.026106
• EISSN: 1550-2376
• ISSN: 1539-3755
• Language: English