diff --git a/R/centrality.R b/R/centrality.R
index a79e3410bed..ed73c9f6c60 100644
--- a/R/centrality.R
+++ b/R/centrality.R
@@ -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
@@ -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()`.
@@ -438,7 +438,7 @@ betweenness.estimate <- estimate_betweenness
 #'
 betweenness <- function(
   graph,
-  v = V(graph),
+  vids = V(graph),
   directed = TRUE,
   weights = NULL,
   normalized = FALSE,
@@ -446,7 +446,7 @@ betweenness <- function(
 ) {
   res <- betweenness_cutoff_impl(
     graph = graph,
-    vids = v,
+    vids = vids,
     directed = directed,
     weights = weights,
     cutoff = cutoff
@@ -1800,7 +1800,7 @@ harmonic_centrality <- function(
 
 bonpow.dense <- function(
   graph,
-  nodes = V(graph),
+  vids = V(graph),
   loops = FALSE,
   exponent = 1,
   rescale = FALSE,
@@ -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,
@@ -1856,7 +1856,7 @@ bonpow.sparse <- function(
     ev <- ev * sqrt(vcount(graph) / sum((ev)^2))
   }
 
-  ev[as.numeric(nodes)]
+  ev[as.numeric(vids)]
 }
 
 
@@ -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
@@ -1975,22 +1975,22 @@ 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
@@ -1998,7 +1998,7 @@ power_centrality <- function(
 
 alpha.centrality.dense <- function(
   graph,
-  nodes = V(graph),
+  vids = V(graph),
   alpha = 1,
   loops = FALSE,
   exo = 1,
@@ -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,
@@ -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)]
 }
 
 
@@ -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
@@ -2160,7 +2160,7 @@ alpha.centrality.sparse <- function(
 #'
 alpha_centrality <- function(
   graph,
-  nodes = V(graph),
+  vids = V(graph),
   alpha = 1,
   loops = FALSE,
   exo = 1,
@@ -2168,11 +2168,11 @@ alpha_centrality <- function(
   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,
@@ -2182,7 +2182,7 @@ alpha_centrality <- function(
   } else {
     res <- alpha.centrality.dense(
       graph,
-      nodes,
+      vids,
       alpha,
       loops,
       exo,
@@ -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
 }
diff --git a/R/cocitation.R b/R/cocitation.R
index 01d9fdbfdbe..a190ddb8fd2 100644
--- a/R/cocitation.R
+++ b/R/cocitation.R
@@ -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
@@ -53,14 +53,14 @@
 #' 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
@@ -68,14 +68,14 @@ cocitation <- function(graph, v = V(graph)) {
 
 #' @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
diff --git a/R/structural-properties.R b/R/structural-properties.R
index 71f8bc54bbe..083d11bf4a0 100644
--- a/R/structural-properties.R
+++ b/R/structural-properties.R
@@ -875,7 +875,7 @@ mean_distance <- function(
 #'
 #'
 #' @param graph The graph to analyze.
-#' @param v The ids of vertices of which the degree will be calculated.
+#' @param vids The ids of vertices of which the degree will be calculated.
 #' @param mode Character string, \dQuote{out} for out-degree, \dQuote{in} for
 #'   in-degree or \dQuote{total} for the sum of the two. For undirected graphs
 #'   this argument is ignored. \dQuote{all} is a synonym of \dQuote{total}.
@@ -885,7 +885,7 @@ mean_distance <- function(
 #'   number of vertices in the graph.
 #' @inheritParams rlang::args_dots_empty
 #' @return For `degree()` a numeric vector of the same length as argument
-#'   `v`.
+#'   `vids`.
 #'
 #'   For `degree_distribution()` a numeric vector of the same length as the
 #'   maximum degree plus one. The first element is the relative frequency zero
@@ -913,18 +913,18 @@ mean_distance <- function(
 #'
 degree <- function(
   graph,
-  v = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in", "total"),
   loops = TRUE,
   normalized = FALSE
 ) {
   ensure_igraph(graph)
-  v <- as_igraph_vs(graph, v)
+  vids <- as_igraph_vs(graph, vids)
   mode <- igraph.match.arg(mode)
 
   res <- degree_impl(
     graph = graph,
-    vids = v,
+    vids = vids,
     mode = mode,
     loops = loops
   )
@@ -933,7 +933,7 @@ degree <- function(
     res <- res / (vcount(graph) - 1)
   }
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
-    names(res) <- V(graph)$name[v]
+    names(res) <- V(graph)$name[vids]
   }
   res
 }
@@ -1045,7 +1045,7 @@ degree_distribution <- function(graph, cumulative = FALSE, ...) {
 #' histogram.
 #'
 #' @param graph The graph to work on.
-#' @param v Numeric vector, the vertices from which the shortest paths will be
+#' @param vids Numeric vector, the vertices from which the shortest paths will be
 #'   calculated.
 #' @param to Numeric vector, the vertices to which the shortest paths will be
 #'   calculated. By default it includes all vertices. Note that for
@@ -1082,7 +1082,7 @@ degree_distribution <- function(graph, cumulative = FALSE, ...) {
 #'   FALSE, the length of the missing paths are considered as having infinite
 #'   length, making the mean distance infinite as well.
 #' @return For `distances()` a numeric matrix with `length(to)`
-#'   columns and `length(v)` rows. The shortest path length from a vertex to
+#'   columns and `length(vids)` rows. The shortest path length from a vertex to
 #'   itself is always zero. For unreachable vertices `Inf` is included.
 #'
 #'   For `shortest_paths()` a named list with four entries is returned:
@@ -1190,7 +1190,7 @@ degree_distribution <- function(graph, cumulative = FALSE, ...) {
 #'
 distances <- function(
   graph,
-  v = V(graph),
+  vids = V(graph),
   to = V(graph),
   mode = c("all", "out", "in"),
   weights = NULL,
@@ -1212,7 +1212,7 @@ distances <- function(
     mode <- "out"
   }
 
-  v <- as_igraph_vs(graph, v)
+  vids <- as_igraph_vs(graph, vids)
   to <- as_igraph_vs(graph, to)
   mode <- igraph.match.arg(mode)
   mode <- switch(mode, "out" = 1, "in" = 2, "all" = 3)
@@ -1248,7 +1248,7 @@ distances <- function(
   res <- .Call(
     R_igraph_shortest_paths,
     graph,
-    v - 1,
+    vids - 1,
     to - 1,
     as.numeric(mode),
     weights,
@@ -1256,7 +1256,7 @@ distances <- function(
   )
 
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
-    rownames(res) <- V(graph)$name[v]
+    rownames(res) <- V(graph)$name[vids]
     colnames(res) <- V(graph)$name[to]
   }
   res
@@ -1914,7 +1914,7 @@ transitivity <- function(
 #' graph adjacency matrix. For isolated vertices, constraint is undefined.
 #'
 #' @param graph A graph object, the input graph.
-#' @param nodes The vertices for which the constraint will be calculated.
+#' @param vids The vertices for which the constraint will be calculated.
 #'   Defaults to all vertices.
 #' @param weights The weights of the edges. If this is `NULL` and there is
 #'   a `weight` edge attribute this is used. If there is no such edge
@@ -1933,9 +1933,9 @@ transitivity <- function(
 #' g <- sample_gnp(20, 5 / 20)
 #' constraint(g)
 #'
-constraint <- function(graph, nodes = V(graph), weights = NULL) {
+constraint <- function(graph, vids = V(graph), weights = NULL) {
   ensure_igraph(graph)
-  nodes <- as_igraph_vs(graph, nodes)
+  vids <- as_igraph_vs(graph, vids)
 
   if (is.null(weights)) {
     if ("weight" %in% edge_attr_names(graph)) {
@@ -1944,9 +1944,9 @@ constraint <- function(graph, nodes = V(graph), weights = NULL) {
   }
 
   on.exit(.Call(R_igraph_finalizer))
-  res <- .Call(Rx_igraph_constraint, graph, nodes - 1, as.numeric(weights))
+  res <- .Call(Rx_igraph_constraint, graph, vids - 1, as.numeric(weights))
   if (igraph_opt("add.vertex.names") && is_named(graph)) {
-    names(res) <- V(graph)$name[nodes]
+    names(res) <- V(graph)$name[vids]
   }
   res
 }
@@ -2049,7 +2049,7 @@ edge_density <- function(graph, loops = FALSE) {
 ego_size <- function(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 ) {
@@ -2062,7 +2062,7 @@ ego_size <- function(
   .Call(
     R_igraph_neighborhood_size,
     graph,
-    as_igraph_vs(graph, nodes) - 1,
+    as_igraph_vs(graph, vids) - 1,
     as.numeric(order),
     as.numeric(mode),
     mindist
@@ -2105,7 +2105,7 @@ neighborhood_size <- ego_size
 #' @param graph The input graph.
 #' @param order Integer giving the order of the neighborhood. Negative values
 #'   indicate an infinite order.
-#' @param nodes The vertices for which the calculation is performed.
+#' @param vids The vertices for which the calculation is performed.
 #' @param mode Character constant, it specifies how to use the direction of
 #'   the edges if a directed graph is analyzed. For \sQuote{out} only the
 #'   outgoing edges are followed, so all vertices reachable from the source
@@ -2163,7 +2163,7 @@ neighborhood_size <- ego_size
 ego <- function(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 ) {
@@ -2176,7 +2176,7 @@ ego <- function(
   res <- .Call(
     R_igraph_neighborhood,
     graph,
-    as_igraph_vs(graph, nodes) - 1,
+    as_igraph_vs(graph, vids) - 1,
     as.numeric(order),
     as.numeric(mode),
     mindist
@@ -2198,7 +2198,7 @@ neighborhood <- ego
 make_ego_graph <- function(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 ) {
@@ -2211,7 +2211,7 @@ make_ego_graph <- function(
   res <- .Call(
     R_igraph_neighborhood_graphs,
     graph,
-    as_igraph_vs(graph, nodes) - 1,
+    as_igraph_vs(graph, vids) - 1,
     as.numeric(order),
     as.integer(mode),
     mindist
diff --git a/man/alpha.centrality.Rd b/man/alpha.centrality.Rd
index 530eedc75c4..a4fd36b81a1 100644
--- a/man/alpha.centrality.Rd
+++ b/man/alpha.centrality.Rd
@@ -19,10 +19,6 @@ alpha.centrality(
 \item{graph}{The input graph, can be directed or undirected. In undirected
 graphs, edges are treated as if they were reciprocal directed ones.}
 
-\item{nodes}{Vertex sequence, the vertices for which the alpha centrality
-values are returned. (For technical reasons they will be calculated for all
-vertices, anyway.)}
-
 \item{alpha}{Parameter specifying the relative importance of endogenous
 versus exogenous factors in the determination of centrality. See details
 below.}
diff --git a/man/alpha_centrality.Rd b/man/alpha_centrality.Rd
index b41a3223e03..ff70035bb14 100644
--- a/man/alpha_centrality.Rd
+++ b/man/alpha_centrality.Rd
@@ -6,7 +6,7 @@
 \usage{
 alpha_centrality(
   graph,
-  nodes = V(graph),
+  vids = V(graph),
   alpha = 1,
   loops = FALSE,
   exo = 1,
@@ -19,7 +19,7 @@ alpha_centrality(
 \item{graph}{The input graph, can be directed or undirected. In undirected
 graphs, edges are treated as if they were reciprocal directed ones.}
 
-\item{nodes}{Vertex sequence, the vertices for which the alpha centrality
+\item{vids}{Vertex sequence, the vertices for which the alpha centrality
 values are returned. (For technical reasons they will be calculated for all
 vertices, anyway.)}
 
diff --git a/man/betweenness.Rd b/man/betweenness.Rd
index 085d04c5232..cb484536806 100644
--- a/man/betweenness.Rd
+++ b/man/betweenness.Rd
@@ -9,7 +9,7 @@
 \usage{
 betweenness(
   graph,
-  v = V(graph),
+  vids = V(graph),
   directed = TRUE,
   weights = NULL,
   normalized = FALSE,
@@ -27,7 +27,7 @@ edge_betweenness(
 \arguments{
 \item{graph}{The graph to analyze.}
 
-\item{v}{The vertices for which the vertex betweenness will be calculated.}
+\item{vids}{The vertices for which the vertex betweenness will be calculated.}
 
 \item{directed}{Logical, whether directed paths should be considered while
 determining the shortest paths.}
@@ -56,7 +56,7 @@ betweenness. If negative, then there is no such limit.}
 }
 \value{
 A numeric vector with the betweenness score for each vertex in
-\code{v} for \code{betweenness()}.
+\code{vids} for \code{betweenness()}.
 
 A numeric vector with the edge betweenness score for each edge in \code{e}
 for \code{edge_betweenness()}.
diff --git a/man/bonpow.Rd b/man/bonpow.Rd
index 4377272047a..1830d90750b 100644
--- a/man/bonpow.Rd
+++ b/man/bonpow.Rd
@@ -17,9 +17,6 @@ bonpow(
 \arguments{
 \item{graph}{the input graph.}
 
-\item{nodes}{vertex sequence indicating which vertices are to be included in
-the calculation.  By default, all vertices are included.}
-
 \item{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
 loops.  \code{loops} is \code{FALSE} by default.}
diff --git a/man/cocitation.Rd b/man/cocitation.Rd
index 8a6e5f8826d..06f22fdc474 100644
--- a/man/cocitation.Rd
+++ b/man/cocitation.Rd
@@ -5,21 +5,21 @@
 \alias{bibcoupling}
 \title{Cocitation coupling}
 \usage{
-cocitation(graph, v = V(graph))
+cocitation(graph, vids = V(graph))
 
-bibcoupling(graph, v = V(graph))
+bibcoupling(graph, vids = V(graph))
 }
 \arguments{
 \item{graph}{The graph object to analyze}
 
-\item{v}{Vertex sequence or numeric vector, the vertex ids for which the
+\item{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.}
 }
 \value{
-A numeric matrix with \code{length(v)} lines and
+A numeric matrix with \code{length(vids)} lines and
 \code{vcount(graph)} columns. Element \verb{(i,j)} contains the cocitation
-or bibliographic coupling for vertices \code{v[i]} and \code{j}.
+or bibliographic coupling for vertices \code{vids[i]} and \code{j}.
 }
 \description{
 Two vertices are cocited if there is another vertex citing both of them.
@@ -29,10 +29,10 @@ both cite, \code{bibcoupling()} calculates this.
 }
 \details{
 \code{cocitation()} calculates the cocitation counts for the vertices in the
-\code{v} argument and all vertices in the graph.
+\code{vids} argument and all vertices in the graph.
 
 \code{bibcoupling()} calculates the bibliographic coupling for vertices in
-\code{v} and all vertices in the graph.
+\code{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
diff --git a/man/constraint.Rd b/man/constraint.Rd
index ae337644aa5..90ee1aeed23 100644
--- a/man/constraint.Rd
+++ b/man/constraint.Rd
@@ -4,12 +4,12 @@
 \alias{constraint}
 \title{Burt's constraint}
 \usage{
-constraint(graph, nodes = V(graph), weights = NULL)
+constraint(graph, vids = V(graph), weights = NULL)
 }
 \arguments{
 \item{graph}{A graph object, the input graph.}
 
-\item{nodes}{The vertices for which the constraint will be calculated.
+\item{vids}{The vertices for which the constraint will be calculated.
 Defaults to all vertices.}
 
 \item{weights}{The weights of the edges. If this is \code{NULL} and there is
diff --git a/man/degree.Rd b/man/degree.Rd
index 2bd19522f86..b22354eb021 100644
--- a/man/degree.Rd
+++ b/man/degree.Rd
@@ -9,7 +9,7 @@
 \usage{
 degree(
   graph,
-  v = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in", "total"),
   loops = TRUE,
   normalized = FALSE
@@ -30,7 +30,7 @@ degree_distribution(graph, cumulative = FALSE, ...)
 \arguments{
 \item{graph}{The graph to analyze.}
 
-\item{v}{The ids of vertices of which the degree will be calculated.}
+\item{vids}{The ids of vertices of which the degree will be calculated.}
 
 \item{mode}{Character string, \dQuote{out} for out-degree, \dQuote{in} for
 in-degree or \dQuote{total} for the sum of the two. For undirected graphs
@@ -49,7 +49,7 @@ be calculated.}
 }
 \value{
 For \code{degree()} a numeric vector of the same length as argument
-\code{v}.
+\code{vids}.
 
 For \code{degree_distribution()} a numeric vector of the same length as the
 maximum degree plus one. The first element is the relative frequency zero
diff --git a/man/distances.Rd b/man/distances.Rd
index 96ffa1c2860..c4382a77965 100644
--- a/man/distances.Rd
+++ b/man/distances.Rd
@@ -20,7 +20,7 @@ mean_distance(
 
 distances(
   graph,
-  v = V(graph),
+  vids = V(graph),
   to = V(graph),
   mode = c("all", "out", "in"),
   weights = NULL,
@@ -71,7 +71,7 @@ Functions accepting this argument (like \code{mean_distance()}) return
 additional information like the number of disconnected vertex pairs in
 the result when this parameter is set to \code{TRUE}.}
 
-\item{v}{Numeric vector, the vertices from which the shortest paths will be
+\item{vids}{Numeric vector, the vertices from which the shortest paths will be
 calculated.}
 
 \item{to}{Numeric vector, the vertices to which the shortest paths will be
@@ -124,7 +124,7 @@ are reached.}
 }
 \value{
 For \code{distances()} a numeric matrix with \code{length(to)}
-columns and \code{length(v)} rows. The shortest path length from a vertex to
+columns and \code{length(vids)} rows. The shortest path length from a vertex to
 itself is always zero. For unreachable vertices \code{Inf} is included.
 
 For \code{shortest_paths()} a named list with four entries is returned:
diff --git a/man/ego.Rd b/man/ego.Rd
index 69c018240fe..9bd13311743 100644
--- a/man/ego.Rd
+++ b/man/ego.Rd
@@ -16,7 +16,7 @@ connect(graph, order, mode = c("all", "out", "in", "total"))
 ego_size(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -24,7 +24,7 @@ ego_size(
 neighborhood_size(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -32,7 +32,7 @@ neighborhood_size(
 ego(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -40,7 +40,7 @@ ego(
 neighborhood(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -48,7 +48,7 @@ neighborhood(
 make_ego_graph(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -56,7 +56,7 @@ make_ego_graph(
 make_neighborhood_graph(
   graph,
   order = 1,
-  nodes = V(graph),
+  vids = V(graph),
   mode = c("all", "out", "in"),
   mindist = 0
 )
@@ -75,7 +75,7 @@ vertices from which the source vertex is reachable in at most \code{order}
 steps are counted. \sQuote{"all"} ignores the direction of the edges. This
 argument is ignored for undirected graphs.}
 
-\item{nodes}{The vertices for which the calculation is performed.}
+\item{vids}{The vertices for which the calculation is performed.}
 
 \item{mindist}{The minimum distance to include the vertex in the result.}
 }
diff --git a/man/graph.neighborhood.Rd b/man/graph.neighborhood.Rd
index 5e322a65d4b..f013c37a900 100644
--- a/man/graph.neighborhood.Rd
+++ b/man/graph.neighborhood.Rd
@@ -18,8 +18,6 @@ graph.neighborhood(
 \item{order}{Integer giving the order of the neighborhood. Negative values
 indicate an infinite order.}
 
-\item{nodes}{The vertices for which the calculation is performed.}
-
 \item{mode}{Character constant, it specifies how to use the direction of
 the edges if a directed graph is analyzed. For \sQuote{out} only the
 outgoing edges are followed, so all vertices reachable from the source
diff --git a/man/neighborhood.size.Rd b/man/neighborhood.size.Rd
index b52295c6916..7e9aae8cf08 100644
--- a/man/neighborhood.size.Rd
+++ b/man/neighborhood.size.Rd
@@ -18,8 +18,6 @@ neighborhood.size(
 \item{order}{Integer giving the order of the neighborhood. Negative values
 indicate an infinite order.}
 
-\item{nodes}{The vertices for which the calculation is performed.}
-
 \item{mode}{Character constant, it specifies how to use the direction of
 the edges if a directed graph is analyzed. For \sQuote{out} only the
 outgoing edges are followed, so all vertices reachable from the source
diff --git a/man/power_centrality.Rd b/man/power_centrality.Rd
index aeb2c85dd24..927a6e714f4 100644
--- a/man/power_centrality.Rd
+++ b/man/power_centrality.Rd
@@ -6,7 +6,7 @@
 \usage{
 power_centrality(
   graph,
-  nodes = V(graph),
+  vids = V(graph),
   loops = FALSE,
   exponent = 1,
   rescale = FALSE,
@@ -17,7 +17,7 @@ power_centrality(
 \arguments{
 \item{graph}{the input graph.}
 
-\item{nodes}{vertex sequence indicating which vertices are to be included in
+\item{vids}{vertex sequence indicating which vertices are to be included in
 the calculation.  By default, all vertices are included.}
 
 \item{loops}{boolean indicating whether or not the diagonal should be
diff --git a/man/shortest.paths.Rd b/man/shortest.paths.Rd
index f1d4343de3d..801c6089ccb 100644
--- a/man/shortest.paths.Rd
+++ b/man/shortest.paths.Rd
@@ -16,9 +16,6 @@ shortest.paths(
 \arguments{
 \item{graph}{The graph to work on.}
 
-\item{v}{Numeric vector, the vertices from which the shortest paths will be
-calculated.}
-
 \item{to}{Numeric vector, the vertices to which the shortest paths will be
 calculated. By default it includes all vertices. Note that for
 \code{distances()} every vertex must be included here at most once. (This