Node activation temporal network¶
With residual time distribution¶
 random_node_activation_temporal_network(base_net, max_t, iet_dist, res_dist, random_state, size_hint: int = 0)¶

template<temporal_network_edge EdgeT, typename ActivationF, typename ResActivationF, std::uniform_random_bit_generator Gen>
network<EdgeT> random_node_activation_temporal_network(const network<typename EdgeT::StaticProjectionType> &base_net, typename EdgeT::TimeType max_t, ActivationF &&inter_event_time_edge_activation, ResActivationF &&residual_time_edge_activation, Gen &generator, std::size_t size_hint = 0)¶
Generates a random nodeactivation temporal network by activating a each vertex
of base_net
first according to the residual time distribution (for the
first activation time) then based on the interevent time distribution, for
time values from 0 to max_t
(exclusive). At each activation, a random
edge (out)incident to that vertex is selected uniformly at random, and that
edge is activated.
See the list of distributions in the relevant section of the documentation.
The output type depends on the type of base_net
. If a (un)directed network
or a (un)directed hypernetwork is provided, the output will be a (un)directed
temporal network or a (un)directed temporal hypernetwork.
With burnin¶
 random_node_activation_temporal_network(base_net, max_t, iet_dist, random_state, size_hint: int = 0)

template<temporal_network_edge EdgeT, typename ActivationF, std::uniform_random_bit_generator Gen>
network<EdgeT> random_node_activation_temporal_network(const network<typename EdgeT::StaticProjectionType> &base_net, typename EdgeT::TimeType max_t, ActivationF &&inter_event_time_edge_activation, Gen &generator, std::size_t size_hint = 0)¶
Generates a random nodeactivation temporal network without a residual time
distribution, by burningin the interevent time distribution for max_t
time before recording events. The output events have times values from 0 to
max_t
(exclusive).
Each vertex of base_net
is activated based on the interevent time
distribution. At each activation, a random edge (out)incident to that vertex
is selected uniformly at random, and that edge is activated.
See the list of distributions in the relevant section of the documentation.
Warning
This approach might not work on all distributions. It is up to the user to think about whether this suits their use case.