Module random_graphs 

Advanced random graph models and network generation

This module implements a suite of state-of-the-art random graph models:

• Erdős–Rényi G(n,p): edges exist independently with probability p
• Erdős–Rényi G(n,m): exactly m edges chosen uniformly at random
• Barabási–Albert: preferential attachment produces scale-free networks
• Watts–Strogatz: ring-lattice rewiring produces small-world topology
• Random d-regular: uniform random d-regular graph via configuration model
• Hyperbolic random graph (HRG): geometric model in hyperbolic disk
• Stochastic Kronecker graph: iterative tensor-product graph model
• Chung–Lu: random graph with prescribed expected degree sequence

Functions

barabasi_albert

Generate a Barabási–Albert (BA) scale-free graph via preferential attachment.

chung_lu

Generate a Chung–Lu random graph with prescribed expected degree sequence.

erdos_renyi_g_nm

Generate an Erdős–Rényi G(n, m) random graph with exactly m edges.

erdos_renyi_g_np

Generate an Erdős–Rényi G(n, p) random graph.

hyperbolic_random_graph

Generate a hyperbolic random graph (HRG) in the Poincaré disk model.

kronecker_graph

Generate a stochastic Kronecker graph.

random_regular

Generate a uniformly random d-regular graph on n nodes.

watts_strogatz

Generate a Watts–Strogatz small-world graph.