cdlib.benchmark.RDyn¶
- cdlib.benchmark.RDyn(size: int = 300, iterations: int = 5, avg_deg: int = 15, sigma: float = 0.6, lambdad: float = 1, alpha: float = 2.5, paction: float = 1, prenewal: float = 0.8, quality_threshold: float = 0.5, new_node: float = 0.0, del_node: float = 0.0, max_evts: int = 1, simplified: bool = True) [dynetx.DynGraph, <class 'object'>] ¶
RDyn is a syntetic dynamic network generator with time-dependent ground-truth partitions having tunable quality (in terms of conductance). Communities’ ids are aligned across time and a predefined number of merge/plit events are planted in between consecutive stable iterations.
- Parameters:
size – Number of nodes
iterations – Number of stable iterations
avg_deg – Average node degree. Int, default 15
sigma – Percentage of node’s edges within a community. Float, default .6
lambdad – Community size distribution exponent. Float, default 1
alpha – Degree distribution exponent. Float, default 2.5
paction – Probability of node action. Float, default 1
prenewal – Probability of edge renewal. Float, default, .8
quality_threshold – Conductance quality threshold for stable iteration. Float, default .5
new_node – Probability of node appearance. Float, default 0
del_node – Probability of node vanishing. Float, default 0
max_evts – Max number of community events for stable iteration. Int, default 1
simplified – Simplified execution. Boolean, default True. (NB: when True an approximation of the original process is executed - some network characteristics can deviate from the expected ones)
- Returns:
A dynetx DynGraph, the TemporalClustering object
- Example:
>>> from cdlib.benchmark import RDyn >>> G, coms = RDyn(n=300)
- References:
Rossetti, Giulio. “RDyn: graph benchmark handling community dynamics.” Journal of Complex Networks 5.6 (2017): 893-912.
Note
Reference implementation: https://github.com/GiulioRossetti/RDyn