Module network_dynamics 

Network/graph dynamics: epidemic models, opinion dynamics, synchronization.

This module implements:

• NetworkGraph: adjacency list representation, directed/undirected, weighted edges
• SirModel: Susceptible-Infectious-Recovered epidemic model (ODE + network)
• SeirdModel: extended SEIRD epidemic (Exposed + Deceased compartments)
• OpinionDynamics: Deffuant-Weisbuch bounded confidence model
• DeGrootModel: consensus update rule
• VoterModel: majority rule opinion dynamics
• KuramotoModel: coupled oscillators synchronization
• RandomWalkGraph: random walk on graph, hitting time, cover time
• PageRank: power iteration with dangling node handling
• CommunityDetection: modularity Q, greedy modularity maximization
• NetworkRobustness: percolation threshold, giant component fraction
• SmallWorld: Watts-Strogatz rewiring, clustering coefficient, path length

Structs

CommunityDetection

Community detection via modularity maximization.

DeGrootModel

DeGroot consensus model: linear opinion pooling on a network.

KuramotoModel

Kuramoto model of coupled oscillators.

NetworkGraph

Adjacency-list graph for network dynamics simulations.

NetworkRobustness

Network robustness analysis via percolation theory.

OpinionDynamics

Deffuant-Weisbuch bounded confidence opinion dynamics.

PageRank

PageRank via power iteration with teleportation and dangling node handling.

RandomWalkGraph

Random walk on a graph with analysis of hitting and cover times.

SeirdModel

Extended SEIRD epidemic model: Susceptible, Exposed, Infected, Recovered, Deceased.

SeirdState

State of SEIRD model.

SirModel

SIR epidemic model (ODE version for homogeneous mixing).

SirState

State of SIR model at one time step.

SmallWorld

Watts-Strogatz small-world network model.

VoterModel

Voter model: each agent copies a random neighbor’s opinion.