Observing Activated Nodes

Motivation and Starter Code

Instead of just the number of activated nodes, suppose that we want to get the nodes themselves. We start with similar code as the basic example from the README:

import random
import networkx as nx
from cynetdiff.utils import networkx_to_ic_model

# Randomly generate the graph
n = 1_000
p = 0.05
k = 10
graph = nx.watts_strogatz_graph(n, k, p)

# Randomly choose seed nodes
k = 10
nodes = list(graph.nodes)
seeds = random.sample(nodes, k)

# Set the activation probability uniformly and set seeds
model, _ = networkx_to_ic_model(graph, activation_prob=0.2)
model.set_seeds(seeds)

Obtaining the activated nodes

Next, we run the model until completion and can view the nodes that were activated:

model.advance_until_completion()
activated_nodes = set(model.get_activated_nodes())

Counting the number of activations

This functionality is useful in cases where we are interested in how often particular nodes are activated.

Over n_sim = 1_000, we can record the number of simulations where a node is activated:

n_sim = 1_000
times_seen = dict()

for _ in range(n_sim):
    model.reset_model()
    model.advance_until_completion()

    for activated_node in model.get_activated_nodes():
        if activated_node not in times_seen:
            times_seen[activated_node] = 0

        # Increment seen counter for each node
        times_seen[activated_node] += 1

Getting the most frequently activated nodes

The times_seen dictionary now contains the number of simulations each node has been activated in. To get the top l = 50 activated nodes, we can run the following:

nodes_sorted_by_activation_frequency = sorted(
    times_seen.items(), key=lambda pair: -pair[1]
)
l = 50
print(nodes_sorted_by_activation_frequency[:l])