Quick Start#

This guide walks through the basic workflow of extracting a backbone from a network using networkx-backbone.

Import#

The recommended import convention is:

import networkx as nx
import networkx_backbone as nb

Step 1: Create or load a weighted graph#

Most backbone methods operate on weighted graphs. Here we use the built-in Les Miserables co-appearance network:

G = nx.les_miserables_graph()

Step 2: Apply a backbone method#

Apply the disparity filter to compute a p-value for each edge. This returns a copy of the graph with an added "disparity_pvalue" edge attribute:

scored = nb.disparity_filter(G)

Step 3: Filter to extract the backbone#

Use threshold_filter() to keep only edges whose p-value is below a significance threshold:

backbone = nb.threshold_filter(scored, "disparity_pvalue", 0.05)

Step 4: Evaluate the backbone#

Use the measures module to compare the backbone to the original graph:

print(f"Edges kept: {nb.edge_fraction(G, backbone):.1%}")
print(f"Nodes kept: {nb.node_fraction(G, backbone):.1%}")

Disparity filter visualization#

The Sphinx Gallery example below visualizes retained and removed structure after applying disparity_filter followed by threshold_filter.

Disparity Filter Visualization

Score-Then-Filter Graph Comparison Gallery

Other approaches#

Proximity-based scoring: Score edges by how structurally embedded they are using neighborhood similarity, then keep the top fraction:

scored = nb.jaccard_backbone(G)
backbone = nb.fraction_filter(scored, "jaccard", 0.2, ascending=False)

Structural backbone: Score edges by metric-backbone membership and then filter using the boolean keep attribute:

scored = nb.metric_backbone(G)
backbone = nb.boolean_filter(scored, "metric_keep")

Bipartite backbone: Score a bipartite projection, then filter by p-value:

B = nx.davis_southern_women_graph()
women_nodes = [n for n, d in B.nodes(data=True) if d["bipartite"] == 0]
scored = nb.sdsm(B, agent_nodes=women_nodes, projection="hyper")
backbone = nb.threshold_filter(scored, "sdsm_pvalue", 0.05, mode="below")

Comparing methods: Systematically compare multiple backbones:

backbones = {
    "disparity": nb.threshold_filter(nb.disparity_filter(G), "disparity_pvalue", 0.05),
    "mst": nb.boolean_filter(nb.maximum_spanning_tree_backbone(G), "mst_keep"),
}
results = nb.compare_backbones(G, backbones)

Next steps#

Concepts – Learn about the different categories of backbone methods
User Guide – Detailed guides on workflows, method selection, and evaluation
Tutorials – Step-by-step tutorials for each method category
API Reference – Complete API reference for all 65 functions