Weighted Hegselmann-Krause¶
The Weighted Hegselmann-Krause was introduced by Milli et al. in 2021 [1].
This model is a variation of the well-known Hegselmann-Krause (HK). During each interaction a random agenti is selected and the set \(\Gamma_{\epsilon}\) of its neighbors whose opinions differ at most \(\epsilon\) (\(d_{i,j}=|x_i(t)-x_j(t)|\leq \epsilon\)) is identified. Moreover, to account for the heterogeneity of interaction frequency among agent pairs, WHK leverages edge weights, thus capturing the effect of different social bonds’ strength/trust as it happens in reality. To such extent, each edge \((i,j) \in E\), carries a value \(w_{i,j}\in [0,1]\). The update rule then becomes:
The idea behind the WHK formulation is that the opinion of agent \(i\) at time \(t+1\), will be given by the combined effect of his previous belief and the average opinion weighed by its, selected, \(\epsilon\)-neighbor, where \(w_{i,j}\) accounts for \(i\)’s perceived influence/trust of \(j\).
Statuses¶
Node statuses are continuous values in [-1,1].
Parameters¶
Name | Type | Value Type | Default | Mandatory | Description |
---|---|---|---|---|---|
epsilon | Model | float in [0, 1] | — | True | Bounded confidence threshold |
perc_stubborness | Model | float in [0, 1] | 0 | False | Percentage of stubborn agent |
similarity | Model | int in {0, 1} | 0 | False | The method use the feature of the nodes ot not |
option_for_stubbornness | Model | int in {-1,0, 1} | 0 | False | Define distribution of stubborns |
weight | Edge | float in [0, 1] | 0.1 | False | Edge weight |
stubborn | Node | int in {0, 1} | 0 | False | The agent is stubborn or not |
vector | Node | Vector of float in [0, 1] | [] | False | Vector represents the character of the node |
Example¶
In the code below is shown an example of instantiation and execution of an WHK model simulation on a random graph: we an epsilon value of 0.32 and a weight equal 0.2 to all the edges.
import networkx as nx
import ndlib.models.ModelConfig as mc
import ndlib.models.opinions as opn
# Network topology
g = nx.erdos_renyi_graph(1000, 0.1)
# Model selection
model = opn.WHKModel(g)
# Model Configuration
config = mc.Configuration()
config.add_model_parameter("epsilon", 0.32)
# Setting the edge parameters
weight = 0.2
if isinstance(g, nx.Graph):
edges = g.edges
else:
edges = [(g.vs[e.tuple[0]]['name'], g.vs[e.tuple[1]]['name']) for e in g.es]
for e in edges:
config.add_edge_configuration("weight", e, weight)
model.set_initial_status(config)
# Simulation execution
iterations = model.iteration_bunch(20)
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