diff --git a/NEWS.md b/NEWS.md
index f0f56ee..de39cb7 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
 # tidygraph (development version)
 
+* Add `resolution` argument to `group_louvrain()` to mirror the igraph function
+
 # tidygraph 1.2.3
 
 * Small updates to work with new versions of igraph and dplyr
diff --git a/R/group.R b/R/group.R
index 5060e8d..3b95aa8 100644
--- a/R/group.R
+++ b/R/group.R
@@ -22,6 +22,7 @@
 #' @param trials Number of times partition of the network should be attempted
 #' @param steps The number of steps in the random walks
 #' @param options Settings passed on to `igraph::arpack()`
+#' @param resolution Resolution of the modularity function used internally in the algorithm
 #' @param ... arguments passed on to [igraph::cluster_spinglass()]
 #'
 #' @return a numeric vector with the membership for each node in the graph. The
@@ -137,14 +138,14 @@ group_leading_eigen <- function(weights = NULL, steps = -1, label = NULL, option
 #' @describeIn group_graph Group nodes by multilevel optimisation of modularity using [igraph::cluster_louvain()]
 #' @importFrom igraph membership cluster_louvain
 #' @export
-group_louvain <- function(weights = NULL) {
+group_louvain <- function(weights = NULL, resolution = 1) {
   expect_nodes()
   weights <- enquo(weights)
   weights <- eval_tidy(weights, .E())
   if (is.null(weights)) {
     weights <- NA
   }
-  group <- as.integer(membership(cluster_louvain(graph = .G(), weights = weights)))
+  group <- as.integer(membership(cluster_louvain(graph = .G(), weights = weights, resolution = resolution)))
   desc_enumeration(group)
 }
 #' @describeIn group_graph Group nodes by optimising the moldularity score using [igraph::cluster_optimal()]
diff --git a/man/centrality.Rd b/man/centrality.Rd
index 8ac2c08..a1870d1 100644
--- a/man/centrality.Rd
+++ b/man/centrality.Rd
@@ -196,7 +196,7 @@ manual mode for specifying more-or-less any centrality score.
 \itemize{
 \item \code{centrality_alpha()}: Wrapper for \code{\link[igraph:alpha_centrality]{igraph::alpha_centrality()}}
 
-\item \code{centrality_authority()}: Wrapper for \code{\link[igraph:hub_score]{igraph::authority_score()}}
+\item \code{centrality_authority()}: Wrapper for \code{\link[igraph:authority_score]{igraph::authority_score()}}
 
 \item \code{centrality_betweenness()}: Wrapper for \code{\link[igraph:betweenness]{igraph::betweenness()}}
 
diff --git a/man/graph_measures.Rd b/man/graph_measures.Rd
index 407be1e..d28702b 100644
--- a/man/graph_measures.Rd
+++ b/man/graph_measures.Rd
@@ -125,7 +125,7 @@ them.
 
 \item \code{graph_assortativity()}: Measures the propensity of similar nodes to be connected. Wraps \code{\link[igraph:assortativity]{igraph::assortativity()}}
 
-\item \code{graph_automorphisms()}: Calculate the number of automorphisms of the graph. Wraps \code{\link[igraph:count_automorphisms]{igraph::automorphisms()}}
+\item \code{graph_automorphisms()}: Calculate the number of automorphisms of the graph. Wraps \code{\link[igraph:automorphisms]{igraph::automorphisms()}}
 
 \item \code{graph_clique_num()}: Get the size of the largest clique. Wraps \code{\link[igraph:cliques]{igraph::clique_num()}}
 
diff --git a/man/group_graph.Rd b/man/group_graph.Rd
index 2acfe6a..4fce573 100644
--- a/man/group_graph.Rd
+++ b/man/group_graph.Rd
@@ -33,7 +33,7 @@ group_leading_eigen(
   n_groups = NULL
 )
 
-group_louvain(weights = NULL)
+group_louvain(weights = NULL, resolution = 1)
 
 group_optimal(weights = NULL)
 
@@ -68,6 +68,8 @@ initial groups. Will be evaluated in the context of the node data.}
 
 \item{options}{Settings passed on to \code{igraph::arpack()}}
 
+\item{resolution}{Resolution of the modularity function used internally in the algorithm}
+
 \item{...}{arguments passed on to \code{\link[igraph:cluster_spinglass]{igraph::cluster_spinglass()}}}
 }
 \value{