Conditional Composition

Since each compartment identifies an atomic condition it is natural to imagine rules described as trees of compartments.

A compartment tree identify and ordered and disjoint set of conditions that needs to be satisfied to allow status transition (it allows describing an OR logic).

To implement such behaviour we implemented a ConditionalComposition compartment that allows to describe branching. Let’s call it CC.

CC evaluate a guard compartment and, depending from the result it gets evaluate (True or False) move to the evaluation of one of its two child compartments.


Name Value Type Default Mandatory Description
condition Compartment None True Guard Compartment
first_branch Compartment None True Positive Compartment
second_branch Compartment None True Negative Compartment


In the code below is shown the formulation of a model implementing conditional compartment composition.

The rule Susceptible->Infected is implemented using three NodeStochastic compartments chained as follows:

  • If the node n is Susceptible
    • c1: if at least a neighbor of the actual node is Infected, with probability 0.5 evaluate compartment c2 else evaluate compartment c3
    • c2: with probability 0.2 allow the transition to the Infected state
    • c3: with probability 0.1 allow the transition to the Infected state

Indeed, heterogeneous compartment types can be mixed to build more complex scenarios.

import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.CompositeModel as gc
import ndlib.models.compartments as cpm
from ndlib.models.compartments.enums.NumericalType import NumericalType
import ndlib.models.compartments.ConditionalComposition as cif

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

# Composite Model instantiation
model = gc.CompositeModel(g)

# Model statuses

# Compartment definition
c1 = cpm.NodeStochastic(0.5, "Infected")
c2 = cpm.NodeStochastic(0.2)
c3 = cpm.NodeStochastic(0.1)

# Conditional Composition
cc = cif.ConditionalComposition(c1, c2, c3)

# Rule definition
model.add_rule("Susceptible", "Infected", cc)

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

# Simulation execution
iterations = model.iteration_bunch(100)