qbiocode.apps.quvine.data package#

Graph and dataset preparation utilities for QuVINE.

Loading graphs and GWAS tables from disk, preprocessing them (sparsification, largest-connected-component extraction, node subsampling), bounded-radius ego-net expansion, and a library of synthetic graph generators used by qbiocode.apps.quvine.reproducibility to build reproducible benchmarks.

Everything here needs only the base install – numpy, pandas, networkx – so this package is imported eagerly and is unaffected by the [quvine] extra.

Note

Graph-complexity metrics are not here. They live in QBioCode’s own qbiocode.evaluate_graph() (qbiocode.evaluation.graph_evaluation), which supersedes the graph_complexity module that used to sit alongside these files in the standalone QuVINE distribution.

Submodules:

Summary#

__all__ Classes:

PrepareGraphConfig

PrepareGraphConfig(subsample_nodes: ‘bool’ = False, max_nodes: ‘int’ = 3000, radius: ‘int’ = 2, sparsify_edges: ‘bool’ = True, retain_ratio: ‘float’ = 0.7, max_degree: ‘int’ = 40, scoring: “Literal[‘common_neighbors’, ‘prefer_low_degree’]” = ‘common_neighbors’, verbose: ‘bool’ = False)

__all__ Functions:

`add_hub_nodes`	Add hub nodes to an existing graph.
`expand_neighborhood`	Expand neighborhood around roots up to given hop radius using adjacency dict access.
`generate_barabasi_albert`	Generate a Barabási-Albert scale-free network using preferential attachment.
`generate_bipartite_random`	Generate a random bipartite graph.
`generate_configuration_model_graph`	Generate a simple graph from a configuration model with power-law, log-normal, or Poisson degree sequence.
`generate_core_periphery`	Generate a core-periphery network structure.
`generate_degree_corrected_sbm`	Generate a degree-corrected SBM.
`generate_erdos_renyi`	Generate an Erdős-Rényi random graph.
`generate_graph_with_seeds_and_targets`	Generate a random graph with designated seed and target nodes for embedding evaluation.
`generate_grid_torus_lattice`	Generate a regular grid or torus lattice.
`generate_heterophilic_sbm`	Generate an SBM where p_out / p_in is controlled.
`generate_hierarchical_network`	Generate a hierarchical network with tree-like structure plus random edges.
`generate_modular_network`	Generate a modular network with clear community structure.
`generate_powerlaw_cluster`	Generate a random graph with powerlaw degree distribution and clustering.
`generate_random_geometric`	Generate a random geometric graph in the unit cube.
`generate_random_regular_expander_like`	Generate a random d-regular graph.
`generate_stochastic_block_model`	Generate a stochastic block model graph with community structure.
`generate_watts_strogatz`	Generate a Watts-Strogatz small-world network.
`get_graph_statistics`	Compute comprehensive statistics for a graph.
`induce_subgraph_by_nodes`	Materialize an induced subgraph without using G.subgraph(…) to avoid view contamination.
`keep_largest_connected_component`	Return a connected component while preserving required nodes when possible.
`load_graph`	Loads the largest connected component (giant component) of a network from a CSV file.
`load_gwas_data`
`load_pegasus_results`
`load_seeds_and_targets`
`materialize_undirected_simple_graph`	Return a fully materialized undirected simple nx.Graph (no views, no DiGraph internals).
`prepare_graph`	Step-by-step graph preparation:
`sample_degree_sequence`	Sample a degree sequence and rescale it to target average degree.
`sparsify_edges_biological`	Edge-only sparsification: degree-capped selection prioritizing biologically coherent edges.
`subsample_nodes`
`subsample_nodes_with_protected`	Subsample nodes while preserving a protected set.
`sweep_configuration_model_graphs`	Generate sweep of configuration model graphs.
`sweep_degree_corrected_sbm`	Generate sweep of degree-corrected SBM graphs.
`sweep_grid_torus_lattice`	Generate sweep of grid/torus lattice graphs.
`sweep_heterophilic_sbm`	Generate sweep of heterophilic SBM graphs.
`sweep_random_regular_expander_like`	Generate sweep of random regular graphs.