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Extract Motifs from Network Data

Source: R/motifs-extract.R
extract_motifs.Rd

Extract and analyze triad motifs from network data with flexible filtering, pattern selection, and statistical significance testing. Supports both individual-level analysis (with tna objects or grouped data) and aggregate analysis (with matrices or networks).

Usage

extract_motifs(
  x = NULL,
  data = NULL,
  id = NULL,
  level = NULL,
  edge_method = c("any", "expected", "percent"),
  edge_threshold = 1.5,
  pattern = c("triangle", "network", "closed", "all"),
  exclude_types = NULL,
  include_types = NULL,
  top = NULL,
  by_type = FALSE,
  min_transitions = 5,
  significance = FALSE,
  n_perm = 100,
  seed = NULL
)

# S3 method for class 'cograph_motif_analysis'
print(x, n = 20, ...)

Arguments

x

Input data. Can be:

  • A tna object (supports individual-level analysis)

  • A matrix (aggregate analysis only, unless data and id provided)

  • A cograph_network object

  • An igraph object

data

Optional data.frame containing transition data with an ID column for individual-level analysis. Required columns: from, to, and the column(s) specified in id. If provided, x should be NULL or a matrix of node labels.

id

Column name(s) identifying individuals/groups in data. Can be a single string or character vector for multiple grouping columns. Required for individual-level analysis with non-tna inputs.

level

Analysis level: "individual" counts how many people have each triad, "aggregate" analyzes the summed/single network. Default depends on input: "individual" for tna or when id provided, "aggregate" otherwise.

edge_method

Method for determining edge presence:

"any"

Edge exists if count > 0 (simple, recommended)

"expected"

Edge exists if observed/expected >= threshold

"percent"

Edge exists if edge/total >= threshold

Default "any".

edge_threshold

Threshold value for "expected" or "percent" methods. For "expected", a ratio (e.g., 1.5 means 50\ The default 1.5 is calibrated for this method. For "percent", a proportion (e.g., 0.15 for 15\ When using "percent", set this explicitly (e.g., 0.15). Ignored when edge_method = "any". Default 1.5.

pattern

Pattern filter for which triads to include:

"triangle"

All 3 node pairs must be connected (any direction). Types: 030C, 030T, 120C, 120D, 120U, 210, 300. Default.

"network"

Exclude simple sequential patterns (chains/single edges). Excludes: 003, 012, 021C. Includes stars and triangles.

"closed"

Network without chain patterns. Excludes: 003, 012, 021C, 120C. Similar to network but also removes mutual+chain (120C).

"all"

Include all 16 MAN types, no filtering.

exclude_types

Character vector of MAN types to explicitly exclude. Applied after pattern filter. E.g., c("300") to exclude cliques.

include_types

Character vector of MAN types to exclusively include. If provided, only these types are returned (overrides pattern/exclude).

top

Return only the top N results (by observed count or z-score). NULL returns all results. Default NULL.

by_type

If TRUE, group results by MAN type in output. Default FALSE.

min_transitions

At individual level: minimum total transitions for a person to be included in the analysis. At aggregate level: minimum triad weight to count as present. Default 5.

significance

Logical. Run permutation significance test? Default FALSE.

n_perm

Number of permutations for significance test. Default 100.

seed

Random seed for reproducibility.

Value

A cograph_motif_analysis object (list) containing:

results

Data frame with triad, type, observed count, and (if significance=TRUE) expected, z-score, p-value

type_summary

Summary counts by motif type

params

List of parameters used

level

Analysis level used

MAN Notation

The 16 triad types use MAN (Mutual-Asymmetric-Null) notation where:

  • First digit: number of Mutual (bidirectional) pairs

  • Second digit: number of Asymmetric (one-way) pairs

  • Third digit: number of Null (no edge) pairs

  • Letter suffix: subtype variant (C=cycle, T=transitive, D=down, U=up)

Pattern Types

Triangle patterns (all pairs connected):

030C (cycle), 030T (feed-forward), 120C (regulated cycle), 120D (two out-stars), 120U (two in-stars), 210 (mutual+asymmetric), 300 (clique)

Network patterns (has structure):

021D (out-star), 021U (in-star), 102 (mutual pair), 111D (out-star+mutual), 111U (in-star+mutual), 201 (mutual+in-star), plus all triangle patterns

Sequential patterns (chains):

012 (single edge), 021C (A->B->C chain)

Empty:

003 (no edges)

See also

motifs(), subgraphs(), extract_triads(), motif_census()

Other motifs: extract_triads(), get_edge_list(), motif_census(), motifs(), plot.cograph_motif_analysis(), plot.cograph_motifs(), subgraphs(), triad_census()

Examples

if (FALSE) { # \dontrun{
Mod <- tna::tna(tna::group_regulation)

# Basic: triangles only (default) - individual level for tna
m <- extract_motifs(Mod)
print(m)

# Top 20 with significance testing
m <- extract_motifs(Mod, top = 20, significance = TRUE, n_perm = 100)
plot(m)

# From a matrix (aggregate level)
mat <- Mod$weights
m <- extract_motifs(mat)

# Only feed-forward loops
m <- extract_motifs(Mod, include_types = "030T")

# Triangles but exclude cliques
m <- extract_motifs(Mod, pattern = "triangle", exclude_types = "300")

# From data.frame with ID column (individual level)
# df has columns: id, from, to (and optionally weight)
# m <- extract_motifs(data = df, id = "id")
# m <- extract_motifs(data = df, id = c("group", "person"))
} # }