straph.generators package

Submodules

straph.generators.barabasi_albert module

straph.generators.barabasi_albert.barabasi_albert(t_window, nb_node, occurrence_law_node='poisson', occurrence_param_node=None, presence_law_node='poisson', presence_param_node=None, occurrence_law_link='poisson', occurrence_param_link=None, presence_law_link='poisson', presence_param_link=None, initial_nb_node=1, m_link=1)[source]

Stream Graph generator following a Barabasi-Albert like behavior. The stream graph begins with initial_nb_node all connected, then nb_node-initial_nb_node are added they are connected with m_link existing node with probability p = degree_of_node/sum_of_degrees. Each node occurs following occurrence_law_node(occurrence_param_node), each segmented node has a presence which length follows presence_law_node(presence_param_node), each link occurs following occurrence_law_link( occurrence_param_link) and each segmented link has a presence which length follows presence_law_link(presence_param_link).

Parameters

  • t_window – Time windows of the Stream Graph

  • nb_node – Desired Number of nodes in the stream graphs

  • occurrence_law_node – Random variable for node occurrence (numpy function or ‘poisson’

  • occurrence_param_node – Parameter of the node occurrence law

  • presence_law_node – Random variable for node presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_node – Parameter of the node presence law

  • occurrence_law_link – Random variable for link occurrence (numpy function or ‘poisson’)

  • occurrence_param_link – Parameter of the link occurrence law

  • presence_law_link – Random variable for link presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_link – Parameter of the link presence law

  • initial_nb_node – Parameter of the Barabasi-Albert model : Number of connected nodes initial present (m0)

  • m_link – Parameter of the Barabasi-Albert model : the number of edge when we (preferentially) attach it.

Returns

Generate links presence and occurrence. .. rubric:: References

A. L. Barabási and R. Albert “Emergence of scaling in random networks”, Science 286, pp 509-512, 1999.

Parameters

  • m_link – Parameter of the Barabasi-Albert model : the number of edge when we (preferentially) attach it.

  • initial_nb_nodes – Parameter of the Barabasi-Albert model : Number of connected nodes initial present (m0)

  • nb_node – Number of Nodes

  • node_presence – Node presence

  • occurrence_law_link – Random variable for link occurrence (numpy function or ‘poisson’)

  • occurrence_param_link – Parameter of the link occurrence law

  • presence_law_link – Random variable for link presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_link – Parameter of the link presence law

Returns

straph.generators.barabasi_albert.generate_node_presence(t_window, nb_node, occurrence_law_node, occurrence_param_node, presence_law_node, presence_param_node)[source]

Generate nodes presence and occurrence.

Parameters

  • t_window – Time windows of the Stream Graph

  • nb_node – Number of Nodes

  • occurrence_law_node – Random variable for node occurrence (numpy function or ‘poisson’

  • occurrence_param_node – Parameter of the node occurrence law

  • presence_law_node – Random variable for node presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_node – Parameter of the node presence law

Returns

straph.generators.barabasi_albert.get_intersection(u, v, node_presence)[source]

Get the intersection between the presence of u and v.

Parameters

  • u – First Node

  • v – Second Node

  • node_presence – Node presence

Returns

Interection

Generate the occurrence and the presence of a link given occurrence_law(occurrence_param) and presence_law(presence_param).

Parameters

  • intersec – Intersection between the prense of extremities (realisable interval for link))

  • rep – Number of segmented links

  • dur – Length of each interval corresponding to a segmented link

Returns

link presence

straph.generators.barabasi_albert.random_node_presence(t_windows, rep, plac, dur)[source]

Generate the occurrence and the presence of a node given occurrence_law(occurrence_param) and presence_law(presence_param).

Parameters

  • t_windows – Time window of the Stream Graph

  • rep – Number of segmented nodes

  • plac – Emplacement of each segmented node (sorted array)

  • dur – Length of each interval corresponding to a segmented node

Returns

node presence

straph.generators.erdos_renyi module

straph.generators.erdos_renyi.erdos_renyi(t_window, nb_node, occurrence_law_node='poisson', occurrence_param_node=None, presence_law_node='poisson', presence_param_node=None, occurrence_law_link='poisson', occurrence_param_link=None, presence_law_link='poisson', presence_param_link=None, p_link=None, directed=False, weights_law=False, weights_law_param=False, trips_law=False, trips_law_param=False)[source]

Stream Graph generator following an Erdos-Renyi like behavior. Each link is sampled with probability p_link, each node occurs following occurrence_law_node(occurrence_param_node), each segmented node has a presence which length follows presence_law_node(presence_param_node), each link occurs following occurrence_law_link( occurrence_param_link) and each segmented link has a presence which length follows presence_law_link(presence_param_link).

Parameters

  • trips_law_param –

  • trips_law –

  • weights_law_param –

  • weights_law –

  • t_window – Time windows of the Stream Graph

  • nb_node – Desired Number of nodes in the stream graphs

  • occurrence_law_node – Random variable for node occurrence (numpy function or ‘poisson’

  • occurrence_param_node – Parameter of the node occurrence law

  • presence_law_node – Random variable for node presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_node – Parameter of the node presence law

  • occurrence_law_link – Random variable for link occurrence (numpy function or ‘poisson’)

  • occurrence_param_link – Parameter of the link occurrence law

  • presence_law_link – Random variable for link presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_link – Parameter of the link presence law

  • p_link – Probability of the existence of a link between two random nodes (0<=p_link<1), Erdos Renyi parameter.

  • directed – True for a random directed Stream Graph, False for an undirected Stream Graphs

Returns

straph.generators.erdos_renyi.generate_2core_example(nb_nodes)[source]
straph.generators.erdos_renyi.generate_kcore_example(nb_nodes, k)[source]

Generate links presence and occurrence. .. rubric:: References

P. Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959). E. N. Gilbert, Random Graphs, Ann. Math. Stat., 30, 1141 (1959).

Parameters

  • nb_node – Number of Nodes

  • node_presence – Node presence

  • occurrence_law_link – Random variable for link occurrence (numpy function or ‘poisson’)

  • occurrence_param_link – Parameter of the link occurrence law

  • p_link – Probability of the existence of a link between two random nodes (0<=p_link<1), Erdos Renyi parameter

  • presence_law_link – Random variable for link presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_link – Parameter of the link presence law

  • directed – True for a random directed Stream Graph, False for an undirected Stream Graphs

Returns

straph.generators.erdos_renyi.generate_node_presence(t_window, nb_node, occurrence_law_node, occurrence_param_node, presence_law_node, presence_param_node)[source]

Generate nodes presence and occurrence.

Parameters

  • t_window – Time windows of the Stream Graph

  • nb_node – Number of Nodes

  • occurrence_law_node – Random variable for node occurrence (numpy function or ‘poisson’

  • occurrence_param_node – Parameter of the node occurrence law

  • presence_law_node – Random variable for node presence (numpy function or ‘poisson’ or ‘uniform’)

  • presence_param_node – Parameter of the node presence law

Returns

straph.generators.erdos_renyi.generate_trips(links, link_presence, node_presence, trips_law, trips_law_param)[source]
straph.generators.erdos_renyi.generate_weights(link_presence, weights_law, weights_law_param)[source]
straph.generators.erdos_renyi.get_intersection(u, v, node_presence)[source]

Get the intersection between the presence of u and v.

Parameters

  • u – First Node

  • v – Second Node

  • node_presence – Node presence

Returns

Interection

Return the maximal temporal node corresponding to (b,e,n)

Parameters

  • node_presence –

  • n – node

  • b – beginning of the interval (time)

  • e – ending of the interval (time)

Returns

Maximal temporal node presence corresponding to (b,e,n) : (t0,t1)

Generate a random link with probability p_link.

Parameters

p_link – probability (0<= p_link <1).

Returns

True if the link exists False otherwise

Generate the occurrence and the presence of a link given occurrence_law(occurrence_param) and presence_law(presence_param).

Parameters

  • intersec – Intersection between the prense of extremities (realisable interval for link))

  • rep – Number of segmented links

  • dur – Length of each interval corresponding to a segmented link

Returns

link presence

straph.generators.erdos_renyi.random_node_presence(t_windows, rep, plac, dur)[source]

Generate the occurrence and the presence of a node given occurrence_law(occurrence_param) and presence_law(presence_param).

Parameters

  • t_windows – Time window of the Stream Graph

  • rep – Number of segmented nodes

  • plac – Emplacement of each segmented node (sorted array)

  • dur – Length of each interval corresponding to a segmented node

Returns

node presence

Module contents