Generating edges

This library generate edges in a modular structure based on the paper ... .

Procedure

It follows a two step process:

1. Generating edges inside communities

2. Generating edges between communities

Each of these steps, also follow two subsequent processes:

1. Creating backbone connectivity network for contributing nodes

2. Add a temporal dimension to this static network using poisson point process.

For the first step, the parameters are explained as below:

Backbone connectivity network parameters

Name	Type	Description
alpha	Float	[0.5,1): defines the internal density coefficient
beta	Float	[0,1]: related to community identifialbility

For the second step, the arguments are discussed below:

Poisson point process parameters

Name	Type	Description
lambda_in	Float	Rate of poisson process for generating temporal edges inside communities
lambda_out	Float	Rate of poisson process for generating temporal edges betweek communities

Rewiring noise

Inorder to add imperfections to our linkstream, we introduced a rewiring noise which can be used after the edge generation procedure.

It has only one paramter:

Name	Type	Description
eta	Float	[0,1]: rewiring noise probability

Example

from mosaic_benchmark import ModularLinkStream
M=ModularLinkStream(number_of_nodes=10, t_start=0,t_end=4)
M.add_community([0,1,2,3,4],0,4)
M.add_community([5,6,7,8,9],0,4)

#Full clique inside, No edges outside in backbone connectivity network
M.generate_edges(1,0,1,0)

#Full clique inside and outside in backbone connectivity network (equal rates)
M.clear_edges()
M.generate_edges(1,1,1,1)


#Full clique inside and outside in backbone connectivity network (dissimilar rates)
M.clear_edges()
M.generate_edges(1,1,1,0.01)


# Signifact inside and rare outside in backbone connectivity network (similar rates)
M.clear_edges()
M.generate_edges(0.8,0.1,1,1)

Becarefull that the function gets the attribute with this order:

generate_edges(alpha, beta, lambda_in, lambda_out)