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Network Analysis in ANNEM

Understanding dynamic network formation and evolution

Network Evolution in ANNEM

ANNEM features dynamic networks that evolve based on agent decision similarity and performance. This creates realistic market structures that adapt over time.

# Analyze network evolution
market <- create_annem_market(n_agents = 500)
results <- market$run_simulation(n_steps = 100)
network_metrics <- market$analyze_network_evolution()

# Plot network evolution
network_plots <- plot_network_evolution(network_metrics)
print(network_plots$evolution)

Network Metrics

Key metrics for understanding network structure:

# Calculate comprehensive network metrics
metrics <- calculate_network_metrics(market$network)

# Key metrics
print(paste("Density:", round(metrics$density, 4)))
print(paste("Clustering:", round(metrics$global_clustering, 4)))
print(paste("Avg Path Length:", round(metrics$avg_path_length, 4)))
print(paste("Small-world Sigma:", round(metrics$small_world_sigma, 4)))

Interactive Network Visualization

Create interactive network visualizations:

# Create interactive network visualization
agent_types <- results$agent_types
interactive_net <- create_interactive_network(
  network = market$network,
  agent_types = agent_types,
  width = 800,
  height = 600
)

# Save as HTML file
networkD3::saveNetwork(interactive_net, "my_network.html")

Custom Network Evolution

Control network evolution parameters:

# Custom network evolution
evolved_network <- evolve_network(
  network = market$network,
  agent_decisions = sample_decisions,
  similarity_threshold = 0.9,  # Higher threshold = more selective connections
  evolution_rate = 0.05        # Higher rate = faster evolution
)