Diffusion Trend

The Diffusion Trend plot compares the trends of all the statuses allowed by the diffusive model tested.

Each trend line describes the variation of the number of nodes for a given status iteration after iteration.

class ndlib.viz.mpl.DiffusionTrend.DiffusionTrend(model, trends)
DiffusionTrend.__init__(model, trends)
  • model – The model object
  • trends – The computed simulation trends
DiffusionTrend.plot(filename, percentile)

Generates the plot

  • filename – Output filename
  • percentile – The percentile for the trend variance area
  • statuses – List of statuses to plot. If not specified all statuses trends will be shown.

Below is shown an example of Diffusion Trend description and visualization for the SIR model.

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics as ep
from ndlib.viz.mpl.DiffusionTrend import DiffusionTrend

# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)

# Model selection
model = ep.SIRModel(g)

# Model Configuration
cfg = mc.Configuration()
cfg.add_model_parameter('beta', 0.001)
cfg.add_model_parameter('gamma', 0.01)
cfg.add_model_parameter("fraction_infected", 0.01)

# Simulation execution
iterations = model.iteration_bunch(200)
trends = model.build_trends(iterations)

# Visualization
viz = DiffusionTrend(model, trends)
SIR Diffusion Trend Example

SIR Diffusion Trend Example.