Calculate a motif adjacency matrix for a given motif and motif type, restrict it to its largest connected component, and then run Laplace embedding with specified Laplacian type and number of eigenvalues and eigenvectors.
run_motif_embedding(
adj_mat,
motif_name,
motif_type = c("struc", "func"),
mam_weight_type = c("unweighted", "mean", "product"),
mam_method = c("sparse", "dense"),
num_eigs = 2,
type_lap = c("comb", "rw"),
restrict = TRUE
)A list with 7 entries:
adj_mat: the original adjacency matrix.
motif_adj_mat: the motif adjacency matrix.
comps: the indices of the largest connected component
of the motif adjacency matrix
(if restrict = TRUE).
adj_mat_comps: the original adjacency matrix restricted
to the largest connected component of the motif adjacency matrix
(if restrict = TRUE).
motif_adj_mat_comps: the motif adjacency matrix restricted
to its largest connected component
(if restrict = TRUE).
vals: a length-num_eigs vector containing the
eigenvalues associated with the Laplace embedding
of the (restricted) motif adjacency matrix.
vects: a matrix
containing the eigenvectors associated with the Laplace embedding
of the (restricted) motif adjacency matrix.
Adjacency matrix to be embedded.
Motif used for the motif adjacency matrix.
Type of motif adjacency matrix to use.
One of "func" or "struc".
Weighting scheme for the motif adjacency matrix.
One of "unweighted", "mean" or "product".
The method to use for building the motif adjacency matrix.
One of "sparse" or "dense".
Number of eigenvalues and eigenvectors for the embedding.
Type of Laplacian for the embedding.
One of "comb" or "rw".
Whether or not to restrict the motif adjacency matrix to its largest connected component before embedding.
adj_mat <- matrix(c(1:9), nrow = 3)
run_motif_embedding(adj_mat, "M1", "func")
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