Choosing a Method#

With 65 functions available, selecting the right backbone method depends on the properties of your network and the goals of your analysis.

Decision guide#

Is your graph bipartite?

Use sdsm() (analytical) or fdsm() (Monte Carlo) to score edges in a bipartite projection and then filter by p-value. Fixed null-model variants are also available via fixedfill(), fixedrow(), and fixedcol(). Projection weighting can be selected with projection="simple"|"hyper"|"probs"|"ycn".

Is your graph unweighted?

Use the unweighted module: sparsify() (generic framework), lspar() (local sparsification), or local_degree() (degree-based), then apply boolean_filter() with "sparsify_keep".

Is your graph weighted?

Choose based on your analysis goals:

Statistical significance: Use methods from statistical to test whether edge weights are significant under a null model. These produce p-values that can be filtered at a chosen significance level.
Topological structure: Use methods from structural to extract edges that are important for the network’s topology (shortest paths, spanning trees, planarity).
Neighborhood similarity: Use methods from proximity to score edges based on how similar the neighborhoods of their endpoints are.
Combined approach: Use glab_filter() from hybrid to combine betweenness and degree information.

Method comparison table#

Method	Weighted	Directed	Output Type	Connectivity	Approach
`disparity_filter()`	Yes	Yes	p-value	No guarantee	Statistical
`noise_corrected_filter()`	Yes	Yes	z-score	No guarantee	Statistical
`marginal_likelihood_filter()`	Yes	Yes	p-value	No guarantee	Statistical
`ecm_filter()`	Yes	Yes	p-value	No guarantee	Statistical
`lans_filter()`	Yes	Yes	p-value	No guarantee	Statistical
`multiple_linkage_analysis()`	Yes	Yes	Scored graph	No guarantee	Statistical
`global_threshold_filter()`	Yes	Yes	Scored graph	No guarantee	Structural
`strongest_n_ties()`	Yes	Yes	Scored graph	No guarantee	Structural
`global_sparsification()`	Yes	Yes	Scored graph	No guarantee	Structural
`primary_linkage_analysis()`	Yes	Yes	Scored graph	No guarantee	Structural
`edge_betweenness_filter()`	Yes	Yes	Scored graph	No guarantee	Structural
`node_degree_filter()`	No	Yes	Scored graph	No guarantee	Structural
`high_salience_skeleton()`	Yes	No	Score	No guarantee	Structural
`metric_backbone()`	Yes	No	Scored graph	Depends on filter	Structural
`ultrametric_backbone()`	Yes	No	Scored graph	Depends on filter	Structural
`doubly_stochastic_filter()`	Yes	No	Score	No guarantee	Structural
`h_backbone()`	Yes	No	Scored graph	No guarantee	Structural
`modularity_backbone()`	Yes	No	Node score	No guarantee	Structural
`planar_maximally_filtered_graph()`	Yes	No	Scored graph	Depends on filter	Structural
`maximum_spanning_tree_backbone()`	Yes	No	Scored graph	Depends on filter	Structural
`glab_filter()`	Yes	No	p-value	No guarantee	Hybrid
Proximity methods (12)	No	Yes	Score	No guarantee	Proximity
`sdsm()`	Optional	No	Scored graph	No guarantee	Bipartite
`fdsm()`	Optional	No	Scored graph	No guarantee	Bipartite
`sparsify()`	No	No	Scored graph	Optional (UMST)	Unweighted
`lspar()`	No	No	Scored graph	No guarantee	Unweighted
`local_degree()`	No	No	Scored graph	No guarantee	Unweighted

Choosing a statistical method#

All six statistical methods test edge significance, but they differ in their null models:

disparity_filter(): Assumes each node’s total strength is uniformly distributed across its edges. Works well for heterogeneous networks with broad degree distributions.
noise_corrected_filter(): Uses a binomial framework to model edge weights as outcomes of random processes. Produces z-scores rather than p-values.
marginal_likelihood_filter(): Considers both endpoints of each edge in a binomial null model. Treats weights as integer counts.
ecm_filter(): Uses a maximum-entropy null model that preserves both degree and strength sequences. Most computationally expensive but most principled.
lans_filter(): Nonparametric method using empirical CDFs. Makes no distributional assumptions but relies on sufficient data at each node.
multiple_linkage_analysis(): Local linkage significance method that returns scored edges plus mla_keep flags.

Choosing a Method#

Decision guide#

Method comparison table#

Choosing a statistical method#

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