Dynamic Gillespie SSA¶
Gillespie’s stochastic simulation algorithm is a powerful tool with a successful history for the simulation of reaction processes in homogeneous media and static networks. In the following we explain Vestergaard’s and Génois’s adaption for temporal networks.
How it works¶
You should check out the original paper linked above for a detailed explanation of the alorithm, the study is well-presented and accessible.
To sum up, the algorithm depends on event rates, which in turn depend on the structure of the temporal network and the constitutents of the stochastic simulation model depending on transition events.
After each event, transition rates \(\lambda_i\) are updated according to the rules of the model and the structure of the temporal network; subsequently the total rate \(\Lambda=\sum_i \lambda_i\) is computed. Then, a dimensionless time \(\tau\) is drawn from an exponential distribution. A dimension is attributed to \(\tau\) using the total rate \(\Lambda\). If within the expected change of time \(\tau\) the network structure changes, \(\lambda_i\) and \(\Lambda\) are updated accordingly and the remaining time \(\Delta \tau\) is updated until \(\tau\) is reached.
The adpated Gillespie algorithm hence is a neat modular algorithm in a sense that it solely depends on rates and how they change when the network structure changes. This implies that in an implementation we can focus on two parts: A generalized implementation of the adapted Gillespie algorithm which solely manages the temporal network and determining the next event. This module can then be coupled to a stochastic simulation model which defines the transition events and interprets changes in the network structure to update transition rates.
How it’s implemented¶
In the C++-core
_tacoma, a Gillespie function is implemented to
be supplied with a
temporal_network and a stochastic simulation
The interface between the Gillespie algorithm and a hypothetical
model is defined with the following functions.
model.update_network: This is used by the Gillespie function to tell the model that the network has been updated. The model then has to update its states internally. This has to be an overloaded function with a second version where not only a new edge list is given but lists of edges leaving the network and edges being created can be passed, too.
model.get_rates_and_Lambda: This function computes a vector of event rates and the total rate, based on its internal state. The Gillepsie function then uses these objects to determine the new time and the event happening at this time.
model.make_event: The Gillespie function passes an integer to this function which is the index of the corresponding event in the rate vector. The model then has to update its internal state according to its rules.
Further necessary functions include
model.simulation_ended: ask the model whether the simulation is essentially over (e.g. if there’s no infected anymore)
model.update_observables: ask the model to update its internal observals because the simulation is about to end
model.print: a function to offer a status check for the Gillespie function’s verbose argument
model.reset: the possibility to wind back the model, e.g. for using the same model instance for a second simulation.
How to develop an own model¶
# TODO (this chapter is a bit complicated)