NDlib is built upon networkx and is designed to configure, simulate and visualize diffusion experiments.

Here you can find a few examples to get started with ndlib: for a more comprehensive tutorial check the official Jupyter Notebook.


In order to install the latest version of the library (with visualization facilities) use

pip install ndlib

Chose a Diffusion model

Let’s start importing the required libraries

import networkx as nx
import ndlib.models.epidemics as ep

Once imported the epidemic model module and the networkx library we can initialize the simulation:

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

# Model Selection
model = ep.SIRModel(g)

Configure the simulation

Each model has its own parameters: in order to completely instantiate the simulation we need to specify them using a Configuration object:

import ndlib.models.ModelConfig as mc

# Model Configuration
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('gamma', 0.01)
config.add_model_parameter("fraction_infected", 0.05)

The model configuration allows to specify model parameters (as in this scenario) as well as nodes’ and edges’ ones (e.g. individual thresholds).

Moreover it allows to specify the initial fraction of infected nodes using the fraction_infected model parameter.

It is also possible to explicitly specify an initial set of infected nodes: see ModelConfig for the complete set of use cases.

Execute the simulation

In order to execute the simulation one, or more, iterations must be required using the model.iteration() and/or model.iteration_bunch(n_iterations) methods.

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

Visualize the results

At the end of the simulation the diffusion trend can be visualized as follows (for matplotlib change ndlib.viz.bokeh in ndlib.viz.mpl)

from bokeh.io import output_notebook, show
from ndlib.viz.bokeh.DiffusionTrend import DiffusionTrend

viz = DiffusionTrend(model, trends)
p = viz.plot(width=400, height=400)

Furthermore, a prevalence plot is also made available.

The prevalence plot captures the variation (delta) of nodes for each status in consecutive iterations.

from ndlib.viz.bokeh.DiffusionPrevalence import DiffusionPrevalence

viz2 = DiffusionPrevalence(model, trends)
p2 = viz2.plot(width=400, height=400)

Multiple plots can be combined in a multiplot to provide a complete description of the diffusive process

from ndlib.viz.bokeh.MultiPlot import MultiPlot
vm = MultiPlot()
m = vm.plot()

Multiplots - implemented only for the bokeh provider - are also useful to compare different diffusion models applied to the same graph (as well as a same model instantiated with different parameters)

import ndlib.models.epidemics as ep

vm = MultiPlot()

sis_model = ep.SISModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter('lambda', 0.01)
config.add_model_parameter("fraction_infected", 0.05)
iterations = sis_model.iteration_bunch(200)
trends = sis_model.build_trends(iterations)

viz = DiffusionTrend(sis_model, trends)
p3 = viz.plot(width=400, height=400)

# SI
si_model = ep.SIModel(g)
config = mc.Configuration()
config.add_model_parameter('beta', 0.001)
config.add_model_parameter("fraction_infected", 0.05)
iterations = si_model.iteration_bunch(200)
trends = si_model.build_trends(iterations)

viz = DiffusionTrend(si_model, trends)
p4 = viz.plot(width=400, height=400)

# Threshold
th_model = ep.ThresholdModel(g)
config = mc.Configuration()

# Set individual node threshold
threshold = 0.40
for n in g.nodes():
        config.add_node_configuration("threshold", n, threshold)

config.add_model_parameter("fraction_infected", 0.30)
iterations = th_model.iteration_bunch(60)
trends = th_model.build_trends(iterations)

viz = DiffusionTrend(th_model, trends)
p5 = viz.plot(width=400, height=400)

m = vm.plot()