Run motif-based clustering on the adjacency matrix of a (weighted directed) network, using a specified motif, motif type, weighting scheme, embedding dimension, number of clusters and Laplacian type.
run_motif_clustering(
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,
num_clusts = 2
)A list with 8 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.
clusts: a vector containing integers representing the
cluster assignment of each vertex in the (restricted) graph.
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.
The number of clusters to find.
adj_mat <- matrix(c(1:16), nrow = 4)
run_motif_clustering(adj_mat, "M1", "func")
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