Extract Pathways from Higher-Order Network Objects

Extracts higher-order pathway strings suitable for cograph::plot_simplicial(). Each pathway represents a multi-step dependency: source states lead to a target state.

For net_hon: extracts edges where the source node is higher-order (order > 1), i.e., the transitions that differ from first-order Markov.

For net_hypa: extracts anomalous paths (over- or under-represented relative to the hypergeometric null model).

For net_mogen: extracts all transitions at the optimal order (or a specified order).

Usage

pathways(x, ...)

# S3 method for class 'net_hon'
pathways(x, min_count = 1L, min_prob = 0, top = NULL, order = NULL, ...)

# S3 method for class 'net_hypa'
pathways(x, type = "all", ...)

# S3 method for class 'netobject'
pathways(x, ho_method = c("hon", "hypa"), ...)

# S3 method for class 'net_association_rules'
pathways(x, top = NULL, min_lift = NULL, min_confidence = NULL, ...)

# S3 method for class 'net_link_prediction'
pathways(x, method = NULL, top = 10L, evidence = TRUE, max_evidence = 3L, ...)

# S3 method for class 'net_mogen'
pathways(x, order = NULL, min_count = 1L, min_prob = 0, top = NULL, ...)

Arguments

x: A higher-order network object (net_hon, net_hypa, net_mogen), a netobject, a net_association_rules or a net_link_prediction.
...: Additional arguments passed on to the method.
min_count: Integer. Minimum transition count to include (default: 1). Filters noise from rare observations. Used by the net_hon and net_mogen methods.
min_prob: Numeric. Minimum transition probability to include (default: 0). Useful for filtering weak transitions. Used by the net_hon and net_mogen methods.
top: Integer or NULL. Keep only the top N pathways: ranked by count for net_hon and net_mogen, by lift then confidence for net_association_rules, and by score for net_link_prediction. Default NULL (all) everywhere except net_link_prediction, whose default is 10.
order: Integer or NULL. For net_hon, keep only pathways whose source is of this order; default NULL = every order above 1. For net_mogen, the Markov order to extract; default NULL = the optimal order from model selection.
type: Character. Which anomalies to include: "all" (default), "over", or "under".
ho_method: Character. Higher-order method: "hon" (default) or "hypa".
min_lift: Numeric or NULL. Additional lift filter applied on top of the object's original threshold (default: NULL).
min_confidence: Numeric or NULL. Additional confidence filter (default: NULL).
method: Character or NULL. Which prediction method to use. Default: first method in the object.
evidence: Logical. If TRUE, include common neighbor evidence nodes in each pathway. Default: TRUE.
max_evidence: Integer. Maximum number of evidence nodes per pathway (default: 3).

Value

A character vector of pathway strings in arrow notation (e.g. "A B -> C"), suitable for cograph::plot_simplicial(). Every method returns this shape, and character(0) when nothing survives its filters.

Methods (by class)

pathways(net_hon): Extract higher-order pathways from HON
pathways(net_hypa): Extract anomalous pathways from HYPA
pathways(netobject): Extract pathways from a netobject

Builds a Higher-Order Network (HON) from the netobject's sequence data and returns the higher-order pathways. Requires that the netobject was built from sequence data (has $data).
pathways(net_association_rules): Extract pathways from association rules

Converts association rules {A, B} => {C} into pathway strings "A B -> C" suitable for cograph::plot_simplicial(). Antecedent items become source nodes; consequent items become the target. Rules whose antecedent and consequent share the same item set describe the same simplex, so only the highest-lift rule per item set is returned; top is applied after that de-duplication.
pathways(net_link_prediction): Extract pathways from link predictions

Converts predicted links into pathway strings for cograph::plot_simplicial(). When evidence = TRUE (default), each predicted edge A -> B is enriched with common neighbors that structurally support the prediction, producing "A cn1 cn2 -> B".
pathways(net_mogen): Extract transition pathways from MOGen

Examples

# \donttest{
seqs <- list(c("A","B","C","D"), c("A","B","C","A"))
hon <- build_hon(seqs, max_order = 3)
pw <- pathways(hon)
# }

trans <- list(c("A","B","C"), c("A","B"), c("B","C","D"), c("A","C","D"))
rules <- association_rules(trans, min_support = 0.3, min_confidence = 0.3,
                           min_lift = 0)
pathways(rules)
#> [1] "D -> C" "A -> B" "A -> C" "B -> C"

seqs <- data.frame(
  V1 = sample(LETTERS[1:5], 50, TRUE),
  V2 = sample(LETTERS[1:5], 50, TRUE),
  V3 = sample(LETTERS[1:5], 50, TRUE)
)
net <- build_network(seqs, method = "relative")
pred <- predict_links(net, methods = "common_neighbors")
pathways(pred)
#> [1] "C A B D -> E"