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Standardize vertex selector parameter name to vids #2458

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Initial plan
Copilot Oct 28, 2025
3383cfd
Standardize vertex selector parameter names to `vids`
Copilot Oct 28, 2025
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48 changes: 24 additions & 24 deletions R/centrality.R
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Original file line number Diff line number Diff line change
Expand Up @@ -390,7 +390,7 @@ betweenness.estimate <- estimate_betweenness
#' @aliases betweenness.estimate
#' @aliases edge.betweenness.estimate
#' @param graph The graph to analyze.
#' @param v The vertices for which the vertex betweenness will be calculated.
#' @param vids The vertices for which the vertex betweenness will be calculated.
#' @param directed Logical, whether directed paths should be considered while
#' determining the shortest paths.
#' @param weights Optional positive weight vector for calculating weighted
Expand All @@ -411,7 +411,7 @@ betweenness.estimate <- estimate_betweenness
#' @param cutoff The maximum shortest path length to consider when calculating
#' betweenness. If negative, then there is no such limit.
#' @return A numeric vector with the betweenness score for each vertex in
#' `v` for `betweenness()`.
#' `vids` for `betweenness()`.
#'
#' A numeric vector with the edge betweenness score for each edge in `e`
#' for `edge_betweenness()`.
Expand All @@ -438,15 +438,15 @@ betweenness.estimate <- estimate_betweenness
#'
betweenness <- function(
graph,
v = V(graph),
vids = V(graph),
directed = TRUE,
weights = NULL,
normalized = FALSE,
cutoff = -1
) {
res <- betweenness_cutoff_impl(
graph = graph,
vids = v,
vids = vids,
directed = directed,
weights = weights,
cutoff = cutoff
Expand Down Expand Up @@ -1800,7 +1800,7 @@ harmonic_centrality <- function(

bonpow.dense <- function(
graph,
nodes = V(graph),
vids = V(graph),
loops = FALSE,
exponent = 1,
rescale = FALSE,
Expand All @@ -1823,12 +1823,12 @@ bonpow.dense <- function(
} else {
ev <- ev * sqrt(n / sum((ev)^2))
}
ev[as.numeric(nodes)]
ev[as.numeric(vids)]
}

bonpow.sparse <- function(
graph,
nodes = V(graph),
vids = V(graph),
loops = FALSE,
exponent = 1,
rescale = FALSE,
Expand Down Expand Up @@ -1856,7 +1856,7 @@ bonpow.sparse <- function(
ev <- ev * sqrt(vcount(graph) / sum((ev)^2))
}

ev[as.numeric(nodes)]
ev[as.numeric(vids)]
}


Expand Down Expand Up @@ -1914,7 +1914,7 @@ bonpow.sparse <- function(
#' is important to think about the edge direction and what it represents.
#'
#' @param graph the input graph.
#' @param nodes vertex sequence indicating which vertices are to be included in
#' @param vids vertex sequence indicating which vertices are to be included in
#' the calculation. By default, all vertices are included.
#' @param loops boolean indicating whether or not the diagonal should be
#' treated as valid data. Set this true if and only if the data can contain
Expand Down Expand Up @@ -1975,30 +1975,30 @@ bonpow.sparse <- function(
#'
power_centrality <- function(
graph,
nodes = V(graph),
vids = V(graph),
loops = FALSE,
exponent = 1,
rescale = FALSE,
tol = 1e-7,
sparse = TRUE
) {
nodes <- as_igraph_vs(graph, nodes)
vids <- as_igraph_vs(graph, vids)
if (sparse) {
res <- bonpow.sparse(graph, nodes, loops, exponent, rescale, tol)
res <- bonpow.sparse(graph, vids, loops, exponent, rescale, tol)
} else {
res <- bonpow.dense(graph, nodes, loops, exponent, rescale, tol)
res <- bonpow.dense(graph, vids, loops, exponent, rescale, tol)
}

if (igraph_opt("add.vertex.names") && is_named(graph)) {
names(res) <- vertex_attr(graph, "name", nodes)
names(res) <- vertex_attr(graph, "name", vids)
}

res
}

alpha.centrality.dense <- function(
graph,
nodes = V(graph),
vids = V(graph),
alpha = 1,
loops = FALSE,
exo = 1,
Expand Down Expand Up @@ -2037,12 +2037,12 @@ alpha.centrality.dense <- function(
diag(id) <- 1

ev <- solve(id - alpha * d, tol = tol) %*% exo
ev[as.numeric(nodes)]
ev[as.numeric(vids)]
}

alpha.centrality.sparse <- function(
graph,
nodes = V(graph),
vids = V(graph),
alpha = 1,
loops = FALSE,
exo = 1,
Expand Down Expand Up @@ -2092,7 +2092,7 @@ alpha.centrality.sparse <- function(
M3 <- M2 - alpha * M
r <- Matrix::solve(M3, tol = tol, exo)

r[as.numeric(nodes)]
r[as.numeric(vids)]
}


Expand All @@ -2114,7 +2114,7 @@ alpha.centrality.sparse <- function(
#'
#' @param graph The input graph, can be directed or undirected. In undirected
#' graphs, edges are treated as if they were reciprocal directed ones.
#' @param nodes Vertex sequence, the vertices for which the alpha centrality
#' @param vids Vertex sequence, the vertices for which the alpha centrality
#' values are returned. (For technical reasons they will be calculated for all
#' vertices, anyway.)
#' @param alpha Parameter specifying the relative importance of endogenous
Expand Down Expand Up @@ -2160,19 +2160,19 @@ alpha.centrality.sparse <- function(
#'
alpha_centrality <- function(
graph,
nodes = V(graph),
vids = V(graph),
alpha = 1,
loops = FALSE,
exo = 1,
weights = NULL,
tol = 1e-7,
sparse = TRUE
) {
nodes <- as_igraph_vs(graph, nodes)
vids <- as_igraph_vs(graph, vids)
if (sparse) {
res <- alpha.centrality.sparse(
graph,
nodes,
vids,
alpha,
loops,
exo,
Expand All @@ -2182,7 +2182,7 @@ alpha_centrality <- function(
} else {
res <- alpha.centrality.dense(
graph,
nodes,
vids,
alpha,
loops,
exo,
Expand All @@ -2191,7 +2191,7 @@ alpha_centrality <- function(
)
}
if (igraph_opt("add.vertex.names") && is_named(graph)) {
names(res) <- vertex_attr(graph, "name", nodes)
names(res) <- vertex_attr(graph, "name", vids)
}
res
}
26 changes: 13 additions & 13 deletions R/cocitation.R
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Original file line number Diff line number Diff line change
Expand Up @@ -27,22 +27,22 @@
#' both cite, `bibcoupling()` calculates this.
#'
#' `cocitation()` calculates the cocitation counts for the vertices in the
#' `v` argument and all vertices in the graph.
#' `vids` argument and all vertices in the graph.
#'
#' `bibcoupling()` calculates the bibliographic coupling for vertices in
#' `v` and all vertices in the graph.
#' `vids` and all vertices in the graph.
#'
#' Calculating the cocitation or bibliographic coupling for only one vertex
#' costs the same amount of computation as for all vertices. This might change
#' in the future.
#'
#' @param graph The graph object to analyze
#' @param v Vertex sequence or numeric vector, the vertex ids for which the
#' @param vids Vertex sequence or numeric vector, the vertex ids for which the
#' cocitation or bibliographic coupling values we want to calculate. The
#' default is all vertices.
#' @return A numeric matrix with `length(v)` lines and
#' @return A numeric matrix with `length(vids)` lines and
#' `vcount(graph)` columns. Element `(i,j)` contains the cocitation
#' or bibliographic coupling for vertices `v[i]` and `j`.
#' or bibliographic coupling for vertices `vids[i]` and `j`.
#' @author Gabor Csardi \email{csardi.gabor@@gmail.com}
#' @family cocitation
#' @export
Expand All @@ -53,29 +53,29 @@
#' cocitation(g)
#' bibcoupling(g)
#'
cocitation <- function(graph, v = V(graph)) {
cocitation <- function(graph, vids = V(graph)) {
ensure_igraph(graph)

v <- as_igraph_vs(graph, v)
vids <- as_igraph_vs(graph, vids)
on.exit(.Call(R_igraph_finalizer))
res <- .Call(Rx_igraph_cocitation, graph, v - 1)
res <- .Call(Rx_igraph_cocitation, graph, vids - 1)
if (igraph_opt("add.vertex.names") && is_named(graph)) {
rownames(res) <- vertex_attr(graph, "name", v)
rownames(res) <- vertex_attr(graph, "name", vids)
colnames(res) <- vertex_attr(graph, "name")
}
res
}

#' @rdname cocitation
#' @export
bibcoupling <- function(graph, v = V(graph)) {
bibcoupling <- function(graph, vids = V(graph)) {
ensure_igraph(graph)

v <- as_igraph_vs(graph, v)
vids <- as_igraph_vs(graph, vids)
on.exit(.Call(R_igraph_finalizer))
res <- .Call(Rx_igraph_bibcoupling, graph, v - 1)
res <- .Call(Rx_igraph_bibcoupling, graph, vids - 1)
if (igraph_opt("add.vertex.names") && is_named(graph)) {
rownames(res) <- vertex_attr(graph, "name", v)
rownames(res) <- vertex_attr(graph, "name", vids)
colnames(res) <- vertex_attr(graph, "name")
}
res
Expand Down
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