Independent Cascades

The Independent Cascades model was introduced by Kempe et all in 2003 [1].

This model starts with an initial set of active nodes A0: the diffusive process unfolds in discrete steps according to the following randomized rule:

  • When node v becomes active in step t, it is given a single chance to activate each currently inactive neighbor w; it succeeds with a probability p(v,w).
  • If w has multiple newly activated neighbors, their attempts are sequenced in an arbitrary order.
  • If v succeeds, then w will become active in step t + 1; but whether or not v succeeds, it cannot make any further attempts to activate w in subsequent rounds.
  • The process runs until no more activations are possible.


During the simulation a node can experience the following statuses:

Name Code
Susceptible 0
Infected 1
Removed 2


Name Type Value Type Default Mandatory Description
Edge threshold Edge float in [0, 1] 0.1 False Edge threshold

The initial infection status can be defined via:

  • fraction_infected: Model Parameter, float in [0, 1]
  • Infected: Status Parameter, set of nodes

The two options are mutually exclusive and the latter takes precedence over the former.


The following class methods are made available to configure, describe and execute the simulation:


class ndlib.models.epidemics.IndependentCascadesModel.IndependentCascadesModel(graph, seed=None)

Edge Parameters to be specified via ModelConfig

Parameters:threshold – The edge threshold. As default a value of 0.1 is assumed for all edges.

Model Constructor

Parameters:graph – A networkx graph object
IndependentCascadesModel.set_initial_status(self, configuration)

Set the initial model configuration

Parameters:configuration – a `ndlib.models.ModelConfig.Configuration` object

Reset the simulation setting the actual status to the initial configuration.



Describes the current model parameters (nodes, edges, status)

Returns:a dictionary containing for each parameter class the values specified during model configuration

Specify the statuses allowed by the model and their numeric code

Returns:a dictionary (status->code)

Execute Simulation


Execute a single model iteration

Returns:Iteration_id, Incremental node status (dictionary node->status)
IndependentCascadesModel.iteration_bunch(self, bunch_size)

Execute a bunch of model iterations

  • bunch_size – the number of iterations to execute
  • node_status – if the incremental node status has to be returned.
  • progress_bar – whether to display a progress bar, default False

a list containing for each iteration a dictionary {“iteration”: iteration_id, “status”: dictionary_node_to_status}


In the code below is shown an example of instantiation and execution of an Independent Cascades model simulation on a random graph: we set the initial set of infected nodes as 1% of the overall population, and assign a threshold of 0.1 to all the edges.

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

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

# Model selection
model = ep.IndependentCascadesModel(g)

# Model Configuration
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)

# Setting the edge parameters
threshold = 0.1
for e in g.edges():
    config.add_edge_configuration("threshold", e, threshold)


# Simulation execution
iterations = model.iteration_bunch(200)
  1. Kempe, J. Kleinberg, and E. Tardos, “Maximizing the spread of influence through a social network,” in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD ’03, 2003, pp. 137–146.