network_wrangling: Network Wrangling Verbs

In cograph: Analysis and Visualization of Complex Networks

Network Wrangling Verbs

Description

cograph's verbs for reshaping a network. Every verb takes any supported input (matrix, edge list, igraph, statnet network, tna model, cograph_network), takes its options as named arguments, and returns a cograph_network — or the input format when keep_format = TRUE. There is no pipeline state to activate and nothing to unpack afterwards: use as.data.frame() for the tidy edge or node table.

Value

Each verb returns a cograph_network, except split_components(), which returns a list of them. With keep_format = TRUE a matrix, igraph, statnet network or tna input comes back in that format.

Selecting

filter_nodes(), select_nodes()

Keep nodes by expression, name, index, top-N, neighborhood or component.

filter_edges(), select_edges()

Keep edges by expression, endpoints, bridges, mutuality or top-N.

select_neighbors(), select_component(), select_top(), select_k_core()

Named shorthands for the common selections.

split_components()

One network per connected component.

Weights

threshold_edges()

Keep edges by weight, count, proportion or density.

binarize()

Replace weights with 0/1.

symmetrize()

Combine opposite arcs into one edge.

normalize_weights()

Rescale by row, column, maximum, total, or to [0, 1].

invert_weights()

Turn similarities into distances.

Structure

to_undirected(), to_directed(), reverse_edges()

Change directedness.

remove_isolates()

Drop nodes with no edges.

contract_nodes()

Collapse groups of nodes into one.

spanning_tree(), complement_network()

Derived graphs.

reorder_nodes(), rename_nodes()

Change node order or labels without changing the network.

simplify()

Merge duplicate edges and drop loops.

Editing

add_nodes(), remove_nodes(), add_edges(), remove_edges()

Add and remove.

mutate_nodes(), mutate_edges()

Compute and store attributes.

bind_networks()

Union, intersection or difference of two networks.

Conversion and access

as_cograph(), to_matrix(), to_igraph(), to_network(), to_df(), and as.data.frame() on a cograph_network (see as.data.frame.cograph_network).

Semantics worth knowing

• Filtering edges does not remove nodes. This matches igraph::delete_edges() and tidygraph. Nodes left without edges raise a cograph_isolates_created warning; call remove_isolates() to drop them, or pass keep_isolates = FALSE.

• Undirected results stay undirected. The weight matrix of an undirected result is symmetric, so nothing downstream re-detects it as directed.

• Metadata survives. Node groups, estimation data, layout coordinates and the original source type are carried through every verb.

• Malformed selections are errors. Unknown node names, out-of- range or fractional indices, unknown measure names and a malformed between raise a cograph_bad_selection error rather than warning and returning something plausible.

Related verbs elsewhere

ego_networks(), shortest_paths(), disparity_filter(), detect_communities(), summarize_clusters(), aggregate_layers().

Examples

adj <- matrix(c(0, .5, .8, 0,
                .5, 0, .3, .6,
                .8, .3, 0, .4,
                 0, .6, .4, 0), 4, 4, byrow = TRUE)
rownames(adj) <- colnames(adj) <- c("A", "B", "C", "D")

# One call, named arguments, a tidy table out
as.data.frame(threshold_edges(adj, minimum = 0.4))

# Verbs compose
adj |>
  threshold_edges(minimum = 0.4) |>
  remove_isolates() |>
  mutate_nodes(deg = degree) |>
  as.data.frame(what = "nodes")

cograph documentation built on Sept. 30, 2026, 5:08 p.m.