This set of functions tries to calculate a ranking of the nodes in a graph so that nodes sharing certain topological traits are in proximity in the resulting order. These functions are of great value when composing matrix layouts and arc diagrams but could concievably be used for other things as well.
node_rank_hclust(
method = "average",
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)node_rank_anneal(
cool = 0.5,
tmin = 1e-04,
swap_to_inversion = 0.5,
step_multiplier = 100,
reps = 1,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_branch_bound(
weighted_gradient = FALSE,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_traveller(
method = "two_opt",
...,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_two(
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_mds(
method = "cmdscale",
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_leafsort(
method = "average",
type = "OLO",
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_visual(
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_spectral(
normalized = FALSE,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_spin_out(
step = 25,
nstart = 10,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_spin_in(
step = 5,
sigma = seq(20, 1, length.out = 10),
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_quadratic(
criterion = "2SUM",
reps = 1,
step = 2 * graph_order(),
step_multiplier = 1.1,
temp_multiplier = 0.5,
maxsteps = 50,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_genetic(
...,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
node_rank_dendser(
...,
dist = "shortest",
mode = "out",
weights = NULL,
algorithm = "automatic"
)
An integer vector giving the position of each node in the ranking
The method to use. See Functions section for reference
The algorithm to use for deriving a distance matrix from the graph. One of
"shortest" (default): Use the shortest path between all nodes
"euclidean": Calculate the L2 norm on the adjacency matrix of the graph
"manhattan": Calculate the L1 norm on the adjacency matrix of the graph
"maximum": Calculate the supremum norm on the adjacenecy matrix of the graph
"canberra": Calculate a weighted manhattan distance on the adjacency matrix of the graph
"binary": Calculate distance as the proportion of agreement between nodes based on the adjacency matrix of the graph
or a function that takes a tbl_graph and return a dist object with a size
matching the order of the graph.
Which edges should be included in the distance calculation. For
distance measures based on the adjacency matrix, 'out' will use the matrix
as is, 'in' will use the transpose, and 'all' will take the mean of the
two. Defaults to 'out'. Ignored for undirected graphs.
An edge variable to use as weight for the shortest path
calculation if dist = 'shortest'
The algorithm to use for the shortest path calculation if
dist = 'shortest'
cooling rate
minimum temperature
Proportion of swaps in local neighborhood search
Multiplication factor for number of iterations per temperature
Number of repeats with random initialisation
minimize the weighted gradient measure? Defaults to FALSE
Arguments passed on to other algorithms. See Functions section for reference
The type of leaf reordering, either 'GW' to use the "GW" method or 'OLO' to use the "OLO" method (both in seriation)
Should the normalized laplacian of the similarity matrix be used?
The number iterations to run per initialisation
The number of random initialisations to perform
The variance around the diagonal to use for the weight matrix. Either a single number or a decreasing sequence.
The criterion to minimize. Either "LS" (Linear Seriation Problem), "2SUM" (2-Sum Problem), "BAR" (Banded Anti-Robinson form), or "Inertia" (Inertia criterion)
Temperature multiplication factor between 0 and 1
The upper bound of iterations
node_rank_hclust(): Use hierarchical clustering to rank nodes (see stats::hclust() for allowed methods)
node_rank_anneal(): Use simulated annealing based on the "ARSA" method in seriation
node_rank_branch_bound(): Use branch and bounds strategy to minimize the gradient measure (only feasable for small graphs). Will use "BBURCG" or "BBWRCG" in seriation dependent on the weighted_gradient argument
node_rank_traveller(): Minimize hamiltonian path length using a travelling salesperson solver. See the the solve_TSP function in TSP for an overview of possible arguments
node_rank_two(): Use Rank-two ellipse seriation to rank the nodes. Uses "R2E" method in seriation
node_rank_mds(): Rank by multidimensional scaling onto one dimension. method = 'cmdscale' will use the classic scaling from stats, method = 'isoMDS' will use isoMDS from MASS, and method = 'sammon' will use sammon from MASS
node_rank_leafsort(): Minimize hamiltonian path length by reordering leafs in a hierarchical clustering. Method refers to the clustering algorithm (either 'average', 'single', 'complete', or 'ward')
node_rank_visual(): Use Prim's algorithm to find a minimum spanning tree giving the rank. Uses the "VAT" method in seriation
node_rank_spectral(): Minimize the 2-sum problem using a relaxation approach. Uses the "Spectral" or "Spectral_norm" methods in seriation depending on the value of the norm argument
node_rank_spin_out(): Sorts points into neighborhoods by pushing large distances away from the diagonal. Uses the "SPIN_STS" method in seriation
node_rank_spin_in(): Sorts points into neighborhoods by concentrating low distances around the diagonal. Uses the "SPIN_NH" method in seriation
node_rank_quadratic(): Use quadratic assignment problem formulations to minimize criterions using simulated annealing. Uses the "QAP_LS", "QAP_2SUM", "QAP_BAR", or "QAP_Inertia" methods from seriation dependant on the criterion argument
node_rank_genetic(): Optimizes different criteria based on a genetic algorithm. Uses the "GA" method from seriation. See register_GA for an overview of relevant arguments
node_rank_dendser(): Optimizes different criteria based on heuristic dendrogram seriation. Uses the "DendSer" method from seriation. See register_DendSer for an overview of relevant arguments
graph <- create_notable('zachary') %>%
mutate(rank = node_rank_hclust())
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