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Decompose an igraph object into base R components

Source: R/decompose_rs.R
decompose_igraph.Rd

decompose_igraph extracts the adjacency matrix and any vertex/edge attributes from an igraph object, returning them in a standardized list format.

Usage

decompose_igraph(grph, weight = NULL)

Arguments

grph

An igraph object to be decomposed.

weight

Character string specifying the edge attribute to use as weights in the adjacency matrix. If NULL (default), the unweighted adjacency matrix is returned with 1s for edges and 0s for non-edges.

Value

A list containing four elements:

  • adj_mat: The adjacency matrix extracted from the igraph object

    • For unipartite graphs: Square matrix of dimension n×n

    • For bipartite graphs: Rectangular matrix of dimension n₁×n₂

    • Values are edge weights if specified, otherwise 0/1

  • ndata: A data frame of vertex attributes, or NULL if none exist

    • Always includes an 'actor' column with vertex names

    • Additional columns for each vertex attribute

  • ddata: A data frame of edge attributes, or NULL if none exist

    • Columns 'from' and 'to' specify edge endpoints

    • Additional columns for each edge attribute

  • weight: The edge attribute name used for weights, if provided

Details

The function handles both unipartite and bipartite graphs appropriately:

Graph type detection:

  • Bipartite graphs must have a logical 'type' vertex attribute

  • If the 'type' attribute exists but is not logical, the graph is treated as unipartite with a warning

Vertex naming:

If the graph lacks vertex names, default names are assigned:

  • Unipartite graphs: "a1", "a2", ..., "an"

  • Bipartite graphs: "r1", "r2", ... for type 1; "c1", "c2", ... for type 2

Existing vertex names are always preserved and used in the output.

Matrix extraction:

  • Unipartite: Uses as_adjacency_matrix() to get n×n matrix

  • Bipartite: Uses as_biadjacency_matrix() to get n₁×n₂ matrix where rows correspond to type=FALSE vertices and columns to type=TRUE vertices

Attribute handling:

All vertex and edge attributes are preserved in the output data frames. System attributes (like 'name' and 'type') are included alongside user-defined attributes.

Note

For longitudinal networks with changing actor composition, explicitly set vertex names before decomposition to ensure consistent actor identification across time periods.

The adjacency matrix is always returned as a standard R matrix (not sparse), which may have memory implications for very large graphs.

When edge attributes are used as weights, ensure they contain numeric values. Non-numeric edge attributes will cause an error.

Author

Cassy Dorff, Shahryar Minhas