Generalised Threshold¶
The Generalised Threshold model was introduced in 2017 by Török and Kertesz [1].
In this model, during an epidemics, a node is allowed to change its status from Susceptible to Infected.
The model is instantiated on a graph having a non-empty set of infected nodes.
The model is defined as follows:
- At time t nodes become Infected with rate mu t/tau
- Nodes for which the ratio of the active friends dropped below the threshold are moved to the Infected queue
- Nodes in the Infected queue become infected with rate tau. If this happens check all its friends for threshold
Statuses¶
During the simulation a node can experience the following statuses:
Name | Code |
---|---|
Susceptible | 0 |
Infected | 1 |
Parameters¶
Name | Type | Value Type | Default | Mandatory | Description |
---|---|---|---|---|---|
threshold | Node | float in [0, 1] | 0.1 | False | Individual threshold |
tau | Model | int | True | Adoption threshold rate | |
mu | Model | int | True | Exogenous timescale |
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.
Example¶
In the code below is shown an example of instantiation and execution of a Threshold 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 0.25 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.GeneralisedThresholdModel(g)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter('fraction_infected', 0.1)
config.add_model_parameter('tau', 5)
config.add_model_parameter('mu', 5)
# Setting node parameters
threshold = 0.25
for i in g.nodes():
config.add_node_configuration("threshold", i, threshold)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(200)
[1] | János Török and János Kertész “Cascading collapse of online social networks” Scientific reports, vol. 7 no. 1, 2017 |