The Profile model was introduced in 2017 by Milli et al. [1].

The Profile model assumes that the diffusion process is only apparent; each node decides to adopt or not a given behavior – once known its existence – only on the basis of its own interests.

In this scenario the peer pressure is completely ruled out from the overall model: it is not important how many of its neighbors have adopted a specific behaviour, if the node does not like it, it will not change its interests.

Each node has its own profile describing how likely it is to accept a behaviour similar to the one that is currently spreading.

The diffusion process starts from a set of nodes that have already adopted a given behaviour S:

  • for each of the susceptible nodes’ in the neighborhood of a node u in S an unbalanced coin is flipped, the unbalance given by the personal profile of the susceptible node;
  • if a positive result is obtained the susceptible node will adopt the behaviour, thus becoming infected.
  • if the blocked status is enabled, after having rejected the adoption with probability blocked a node becomes immune to the infection.
  • every iteration adopter_rate percentage of nodes spontaneous became infected to endogenous effects.


During the simulation a node can experience the following statuses:

Name Code
Susceptible 0
Infected 1
Blocked -1


Name Type Value Type Default Mandatory Description
profile Node float in [0, 1] 0.1 False Node profile
blocked Model float in [0, 1] 0 False Blocked nodes
adopter_rate Model float in [0, 1] 0 False Autonomous adoption

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.ProfileModel.ProfileModel(graph, seed=None)
Node Parameters to be specified via ModelConfig
Parameters:profile – The node profile. As default a value of 0.1 is assumed for all nodes.

Model Constructor

Parameters:graph – A networkx graph object
ProfileModel.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)
ProfileModel.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 a Profile model simulation on a random graph: we set the initial infected node set to the 10% of the overall population and assign a profile of 0.15 to all the nodes.

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.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('blocked', 0)
config.add_model_parameter('adopter_rate', 0)
config.add_model_parameter('fraction_infected', 0.1)

# Setting nodes parameters
profile = 0.15
for i in g.nodes():
    config.add_node_configuration("profile", i, profile)


# Simulation execution
iterations = model.iteration_bunch(200)
[1]Letizia Milli, Giulio Rossetti, Dino Pedreschi, Fosca Giannotti, “Information Diffusion in Complex Networks: The Active/Passive Conundrum,” Proceedings of International Conference on Complex Networks and their Applications, (pp. 305-313). Springer, Cham. 2017