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Reticula
Reticula
  • Installation
  • Quick Start
  • Networks and network types
  • Input/Output and interoperation
  • Randomness
  • Generating networks
    • Path graphs
    • Cycle graphs
    • Regular ring lattice
    • Square grid graphs
    • Complete graphs
    • Complete directed graphs
    • Random Erdős–Rényi network
    • Random Barabási–Albert network
    • Random k-regular network
    • Random degree-sequence network
    • Random expected degree-sequence network
    • Fully-mixed temporal network
    • Link activation temporal network
  • Randomising networks
    • Event shuffling
    • Link Shuffling
    • Timeline Shuffling
  • Network operations
    • Add/Remove operations
    • Occupation operations
    • Cartesian product
    • Graph union
  • Algorithms
    • Vertex degrees
    • Graphicallity
    • Graph properties
    • Assortativity
    • Static network reachability
    • Temporal network reachability
    • Event graphs
  • Examples
    • Isotropic bond-percolation in G(n, p) networks
  • Citing Reticula
  • Bibliography
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Link activation temporal network#

With residual time distribution#

random_link_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_link_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 link-activation temporal network by activating the edges in base_net first according to the residual time distribution (for the first activation time) then based on the inter-event time distribution, for time values from 0 to max_t (exclusive).

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 burn-in#

random_link_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_link_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 link-activation temporal network without a residual time distribution, by burning-in the inter-event time distribution for max_t time before recording events. The output event have times values from 0 to max_t (exclusive).

See the list of distributions in the relevant section of the documentation.

This approach might not work on all distributions. It is up to the user to think about whether this suits their use case.

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Randomising networks
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Fully-mixed temporal network
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On this page
  • Link activation temporal network
    • With residual time distribution
      • random_link_activation_temporal_network()
    • With burn-in
      • random_link_activation_temporal_network()