Module sampling 

Graph Sampling Algorithms

This module provides a comprehensive suite of graph sampling methods including:

• Random walks: Uniform random walk, Node2Vec biased random walk
• Graph sampling: Frontier sampling, forest-fire sampling, snowball sampling
• Subgraph operations: Induced subgraph extraction

All algorithms operate on adjacency-list representations for efficiency.

References

• Leskovec & Faloutsos (2006): Sampling from Large Graphs. KDD 2006.
• Grover & Leskovec (2016): node2vec: Scalable Feature Learning for Networks. KDD 2016.
• Stumpf et al. (2005): Subnets of scale-free networks are not scale-free. PNAS.

Functions

forest_fire_sampling

Forest-fire graph sampling.

frontier_sampling

Frontier-based graph sampling.

induced_subgraph

Extract the induced subgraph on a set of nodes.

node2vec_walk

Perform a Node2Vec biased random walk on a weighted graph.

random_walk

Perform a uniform random walk on an unweighted graph.

snowball_sampling

Snowball (BFS-neighbourhood) sampling.