A powerful edge selection function with lazy computation (only computes metrics actually referenced), multiple selection modes, and structural awareness (bridges, communities, reciprocity).
Usage
select_edges(
x,
...,
top = NULL,
by = "weight",
involving = NULL,
between = NULL,
bridges_only = FALSE,
mutual_only = FALSE,
community = "louvain",
.keep_isolates = FALSE,
keep_format = FALSE,
directed = NULL
)Arguments
- x
Network input: cograph_network, matrix, igraph, network, or tna object.
- ...
Filter expressions using edge columns or computed metrics. Available variables:
- Edge columns
from,to,weight, plus any custom- Computed metrics
abs_weight,from_degree,to_degree,from_strength,to_strength,edge_betweenness,is_bridge,is_mutual,same_community
- top
Integer. Select top N edges by a metric.
- by
Character. Metric for top selection. Default
"weight". Options:"weight","abs_weight","edge_betweenness".- involving
Character or integer. Select edges involving these nodes (by name or index). An edge is selected if either endpoint matches.
- between
List of two character/integer vectors. Select edges between two node sets. Example:
between = list(c("A", "B"), c("C", "D")).- bridges_only
Logical. Select only bridge edges (edges whose removal disconnects the graph). Default FALSE.
- mutual_only
Logical. For directed networks, select only mutual (reciprocated) edges. Default FALSE.
- community
Character. Community detection method for
same_communityvariable. One of"louvain","walktrap","fast_greedy","label_prop","infomap","leiden". Default"louvain".- .keep_isolates
Logical. Keep nodes with no remaining edges? Default FALSE.
- keep_format
Logical. If TRUE, return the same format as input. Default FALSE returns cograph_network.
- directed
Logical or NULL. If NULL (default), auto-detect.
Value
A cograph_network object with selected edges. If keep_format = TRUE,
returns the same type as input.
Details
Selection modes are combined with AND logic:
select_edges(x, top = 10, involving = "A")selects top 10 edges among those involving node AAll criteria must be satisfied for an edge to be selected
Edge metrics are computed lazily - only those actually referenced in expressions or required by selection modes are computed.
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")
# Expression-based (lazy - only computes what's needed)
select_edges(adj, weight > 0.5)
#> Cograph network: 4 nodes, 2 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 2 / 6 (density: 33.3%)
#> Weights: [0.600, 0.800] | mean: 0.700
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> Layout: none
select_edges(adj, abs_weight > 0.4)
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 3 / 6 (density: 50.0%)
#> Weights: [0.500, 0.800] | mean: 0.633
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> A -- B 0.500
#> Layout: none
# Top N edges by weight
select_edges(adj, top = 3)
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 3 / 6 (density: 50.0%)
#> Weights: [0.500, 0.800] | mean: 0.633
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> A -- B 0.500
#> Layout: none
select_edges(adj, top = 3, by = "edge_betweenness")
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 3 / 6 (density: 50.0%)
#> Weights: [0.300, 0.600] | mean: 0.467
#> Strongest edges:
#> B -- D 0.600
#> A -- B 0.500
#> B -- C 0.300
#> Layout: none
# Edges involving specific nodes
select_edges(adj, involving = "A")
#> Cograph network: 3 nodes, 2 edges ( undirected )
#> Source: filtered
#> Nodes (3): A, B, C
#> Edges: 2 / 3 (density: 66.7%)
#> Weights: [0.500, 0.800] | mean: 0.650
#> Strongest edges:
#> A -- C 0.800
#> A -- B 0.500
#> Layout: none
select_edges(adj, involving = c("A", "B"))
#> Cograph network: 4 nodes, 4 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 4 / 6 (density: 66.7%)
#> Weights: [0.300, 0.800] | mean: 0.550
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> A -- B 0.500
#> B -- C 0.300
#> Layout: none
# Edges between two node sets
select_edges(adj, between = list(c("A", "B"), c("C", "D")))
#> Cograph network: 4 nodes, 3 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 3 / 6 (density: 50.0%)
#> Weights: [0.300, 0.800] | mean: 0.567
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> B -- C 0.300
#> Layout: none
# Bridge edges only
select_edges(adj, bridges_only = TRUE)
#> Warning: No edges match the selection criteria.
#> Cograph network: 0 nodes, 0 edges ( undirected )
#> Source: filtered
#> Nodes (0):
#> Layout: none
# Combined: top 3 edges involving A
select_edges(adj, involving = "A", top = 3)
#> Cograph network: 3 nodes, 2 edges ( undirected )
#> Source: filtered
#> Nodes (3): A, B, C
#> Edges: 2 / 3 (density: 66.7%)
#> Weights: [0.500, 0.800] | mean: 0.650
#> Strongest edges:
#> A -- C 0.800
#> A -- B 0.500
#> Layout: none
# Using endpoint degrees
select_edges(adj, from_degree >= 3 | to_degree >= 3)
#> Cograph network: 4 nodes, 5 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 5 / 6 (density: 83.3%)
#> Weights: [0.300, 0.800] | mean: 0.520
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> A -- B 0.500
#> C -- D 0.400
#> B -- C 0.300
#> Layout: none
# Within-community edges
select_edges(adj, same_community)
#> Cograph network: 4 nodes, 2 edges ( undirected )
#> Source: filtered
#> Nodes (4): A, B, C, D
#> Edges: 2 / 6 (density: 33.3%)
#> Weights: [0.600, 0.800] | mean: 0.700
#> Strongest edges:
#> A -- C 0.800
#> B -- D 0.600
#> Layout: none