prepare.geodesic.kk: Prepare full geodesic KK data for repeated layout evaluation

Description

prepare.geodesic.kk() builds the deterministic all-pairs shortest path cache needed to evaluate or optimize the full geodesic Kamada--Kawai objective repeatedly on the same connected graph.

Usage

prepare.geodesic.kk(
  edges = NULL,
  n = NULL,
  adj_list = NULL,
  weight_list = NULL,
  edge_weights = NULL,
  tie_mode = c("single", "average")
)

Value

A list with the all-pairs graph distances, chosen paths, and cached path-edge realizations. The object has class "grip_gkk_prepared".

Arguments

edges: Two-column integer matrix of edges (1-based vertex ids).
n: Number of vertices. If omitted with adj_list, defaults to length(adj_list). If omitted with edges, defaults to max(edges).
adj_list: Adjacency list (1-based) for an undirected graph.
weight_list: Optional parallel list of positive edge weights.
edge_weights: Optional positive edge-weight vector parallel to edges.
tie_mode: Shortest-path aggregation mode. "single" uses one deterministic chosen shortest path per pair. "average" replaces each tied shortest-path family by the exact uniform average over all shortest paths between the pair.

Details

For each unordered vertex pair, the prepared object stores either one deterministic chosen graph shortest path (tie_mode = "single") or the exact uniform average over all tied shortest paths (tie_mode = "average"), together with the graph distance and the corresponding cached edge realization. This is the full all-pairs analogue of the sparse landmark cache used by prepare.landmark.geodesic.kk().