tnetwork.DCD.multi_temporal_scale.generate_multi_temporal_scale¶

tnetwork.DCD.multi_temporal_scale.generate_multi_temporal_scale(nb_steps=5000, nb_nodes=100, nb_com=10, noise=None, max_com_size=None, max_com_duration=None)[source]¶

Generate dynamic graph with stable communities

This benchmark allows to generate temporal networks as described in Detecting Stable Communities in Link Streams at Multiple Temporal Scales. Boudebza, S., Cazabet, R., Nouali, O., & Azouaou, F. (2019)..

To sum up the method, stable communities are generated (i.e., no node change). These communities exist for some periods, but have different temporal scales, i.e., some of them have a high frequency of edges (their edges appear at every step) while others have a lower frequency (i.e., each edge appear only every $t$ steps). To simplify, communities are complete cliques.(but for the low frequency ones, we might observe only a small fraction of their edges in every step)

The basic parameters are the number of steps, number of nodes and number of communities. There are other parameters allowing to modify the random noise, the maximal size of communities and the maximal duration of communities, that are by default assigned with values scaled according to the other parameters.

Parameters:	nb_steps – steps in the graph nb_nodes – total nb nodes nb_com – nb desired communities noise – random noise at each step, i.e. probability for any edge to exist at any step. default,1/(nb_nodes2) max_com_size – max number of nodes. Default: nb_nodes/4 max_com_duration** – max community duration. Default: nb_steps/2
Returns: