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.
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 many it is likely 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:
blocked a node becomes immune to the infection.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:
- percentage_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:
ndlib.models.epidemics.ProfileModel.ProfileModel(graph)Node Parameters to be specified via ModelConfig
| Parameters: | profile – The node profile. As default a value of 0.1 is assumed for all nodes. |
|---|
ProfileModel.__init__(graph)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 |
|---|
ProfileModel.reset(self)Reset the simulation setting the actual status to the initial configuration.
ProfileModel.get_info(self)Describes the current model parameters (nodes, edges, status)
| Returns: | a dictionary containing for each parameter class the values specified during model configuration |
|---|
ProfileModel.get_status_map(self)Specify the statuses allowed by the model and their numeric code
| Returns: | a dictionary (status->code) |
|---|
ProfileModel.iteration(self)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
| Parameters: |
|
|---|---|
| Returns: |
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 0.25 to all the nodes.
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.epidemics.ProfileModel as pr
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = pr.ProfileModel(g)
config = mc.Configuration()
config.add_model_parameter('blocked', 0)
config.add_model_parameter('adopter_rate', 0)
config.add_model_parameter('percentage_infected', 0.1)
# Setting nodes parameters
profile = 0.15
for i in g.nodes():
config.add_node_configuration("profile", i, profile)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(200)
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.
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