Compute the legacy GRIP layout

This function preserves the original local-force wrapper and historical default values that were previously exposed as grip(). Use it for backwards-compatible comparisons or when you explicitly want the pre-global-repulsion behavior.

Usage

legacy.grip(
  edges = NULL,
  n = NULL,
  adj_list = NULL,
  weight_list = NULL,
  edge_weights = NULL,
  dim = 3,
  placement = c("barycenter", "circle"),
  preset = NULL,
  rounds = 20,
  final_rounds = 25,
  num_init = 36,
  num_nbrs = 10,
  r = 0.15,
  s = 3,
  repulsion_factor = 1,
  tinit_factor = 6,
  seed = 6,
  disconnected = c("components", "error")
)

Arguments

edges

Two-column integer matrix of edges (1-based vertex ids).

n

Number of vertices.

adj_list

Adjacency list (1-based) for undirected graphs.

weight_list

Optional parallel list of edge weights (edge lengths). If NULL, all edges are treated as weight 1. All weights must be finite and strictly positive.

edge_weights

Optional vector of edge weights for edges. All weights must be finite and strictly positive.

dim

Layout dimension (2 or 3). Default is 3.

placement

Initial placement strategy. "circle" is only used for 2D.

preset

Optional tuning preset. NULL uses the historical defaults. "carpet" applies a preset tuned for Sierpinski-carpet-like graphs and validated on carpet levels 3 and 4. "mesh" applies a preset tuned for rectangular lattice graphs and validated on 8x8 and 12x12 mesh layouts. "torus" applies a preset tuned for 3D torus layouts and validated on torus sizes from 8x8 through 20x20. "tree" applies a preset tuned for symmetric force-directed layouts of tree-like graphs and validated on binary trees of depths 5 and 6. Presets only fill in tuning arguments that you did not supply explicitly.

rounds

Initial rounds for refinement.

final_rounds

Final rounds for refinement.

num_init

Number of initial vertices in the coarsest level.

num_nbrs

Maximum number of graph-distance neighbors retained for local refinement at each filtration level.

r

Main local temperature adaptation rate in [0, 1].

s

Non-negative boost factor applied when successive displacements have a consistent direction.

repulsion_factor

Non-negative multiplier applied to GRIP's finest-level repulsive force scale. 1 keeps the historical repulsion strength; 0 disables that repulsive term.

tinit_factor

Initial temperature factor.

seed

Optional RNG seed for reproducibility. If NULL, uses current time.

disconnected

How to handle disconnected graphs: "components" (default) lays out each connected component separately and packs them into one coordinate matrix; "error" stops with an error.

Value

A numeric matrix with `n` rows and `dim` columns.

References

Gajer, P. and Kobourov, S.G. (2002). GRIP: Graph dRawing with Intelligent Placement. Journal of Graph Algorithms and Applications, 6(3), 203–224. doi:10.7155/jgaa.00052.

Gajer, P., Goodrich, M.T. and Kobourov, S.G. (2004). A multi-dimensional approach to force-directed layouts of large graphs. Computational Geometry, 29(1), 3–18. doi:10.1016/j.comgeo.2004.03.014.

Examples

edges <- cbind(1:5, 2:6)
coords <- legacy.grip(edges, n = 6, dim = 2,
                             placement = "barycenter",
                             rounds = 5, final_rounds = 5,
                             num_init = 3, num_nbrs = 4,
                             seed = 1)
round(coords, 3)
#>         [,1]    [,2]
#> [1,] -53.098  28.919
#> [2,] -37.729  12.979
#> [3,] -17.147  -5.807
#> [4,]   3.482 -19.158
#> [5,]  21.370 -24.828
#> [6,]  36.977 -31.153