# Graph Generators Examples
Graph generators are essential for testing algorithms and creating benchmarks.
This example demonstrates how to generate various random graphs and analyze their properties.
```rust
use graphina::core::generators::{erdos_renyi_graph, barabasi_albert_graph, watts_strogatz_graph};
use graphina::core::types::Undirected;
use graphina::metrics::{diameter, average_clustering};
fn main() {
let seed = 42;
let n = 1000;
// 1. Erdős-Rényi Graph (Random)
// n=1000, p=0.01
// Features: Low clustering, Poisson degree distribution
println!("Generating Erdős-Rényi graph...");
let er_graph = erdos_renyi_graph::<Undirected>(n, 0.01, seed).unwrap();
println!(" ER Edges: {}", er_graph.edge_count());
println!(" ER Clustering: {:.4}", average_clustering(&er_graph));
// 2. Barabási-Albert Graph (Scale-Free)
// n=1000, edges per new node=3
// Features: Hubs, Power-law degree distribution
println!("\nGenerating Barabási-Albert graph...");
let ba_graph = barabasi_albert_graph::<Undirected>(n, 3, seed).unwrap();
println!(" BA Edges: {}", ba_graph.edge_count());
// BA graphs typically have higher max degree due to hubs
let max_degree = ba_graph.nodes().map(|(id, _)| ba_graph.degree(id)).max().unwrap_or(0);
println!(" BA Max Degree: {}", max_degree);
// 3. Watts-Strogatz Graph (Small World)
// n=1000, k=10 neighbors, p=0.1 rewiring
// Features: High clustering like regular lattice, short paths like random graph
println!("\nGenerating Watts-Strogatz graph...");
let ws_graph = watts_strogatz_graph::<Undirected>(n, 10, 0.1, seed).unwrap();
println!(" WS Edges: {}", ws_graph.edge_count());
println!(" WS Clustering: {:.4}", average_clustering(&ws_graph));
// Comparison summary
println!("\nSummary:");
println!("Scale-free networks (BA) model social networks well.");
println!("Small-world networks (WS) model biological and neural networks well.");
println!("Random graphs (ER) serve as good mathematical baselines.");
}
```