This code has been build for the simulations in Nikita Gutjahr et al 2021 J. Phys. Complex. 2 035023 [link]. The paper explores the influence of topology on the behavior of an existing Griffiths phase in modular networks.
The repository contains python implementations of the SIS epidemic spreading model and the contact process [1], as well as quasistationary [2] implementations of both. Network generation procedures for monodisperse modular networks [3] and hierarchical modular networks [4] are given in networks.py. Topological network measures that have been used, and were not implemented in graph_tool, can be found in topology.py.
[1] Cota, Wesley, and Silvio C. Ferreira. “Optimized Gillespie Algorithms for the Simulation of Markovian Epidemic Processes on Large and Heterogeneous Networks.” Computer Physics Communications 219 (2017): 303–312 [ArXiv]
[2] Martins de Oliveira, Marcelo and Dickman, Ronald "How to simulate the quasistationary state" Phys. Rev. E 71 (1) (2005) [ArXiv]
[3] Cota, Wesley, Géza Ódor, and Silvio C. Ferreira. “Griffiths Phases in Infinite-Dimensional, Non-Hierarchical Modular Networks.” Scientific Reports 8.1 (2018) [ArXiv]
[4] Moretti, Paolo, and Miguel A. Muñoz. “Griffiths Phases and the Stretching of Criticality in Brain Networks.” Nature Communications 4.1 (2013) [ArXiv]
Requires the NumPy, SciPy, Cython and graph-tool libraries.
The dynamics.pyx module needs to be compiled due to optimization with Cython.
For that run python setup.py build_ext --inplace. Afterwards dynamics.pyx can be imported as usual.
The usage is showcased in the jupyter notebook example.ipynb.