diff --git a/R/community.R b/R/community.R
index 901b48d9838..fb6d9f40070 100644
--- a/R/community.R
+++ b/R/community.R
@@ -1,4 +1,3 @@
-
 #' Creates a communities object.
 #'
 #' @description
@@ -469,7 +468,7 @@ membership <- function(communities) {
       which.max(communities$modularity)
     )
   } else {
-    stop("Cannot calculate community membership")
+    cli::cli_abort("Cannot calculate community membership")
   }
   if (igraph_opt("add.vertex.names") && !is.null(communities$names)) {
     names(res) <- communities$names
@@ -681,7 +680,7 @@ modularity.igraph <- function(x, membership, weights = NULL, resolution = 1, dir
   # Argument checks
   ensure_igraph(x)
   if (is.null(membership) || (!is.numeric(membership) && !is.factor(membership))) {
-    stop("Membership is not a numerical vector")
+    cli::cli_abort("Membership is not a numerical vector")
   }
   membership <- as.numeric(membership)
   if (!is.null(weights)) weights <- as.numeric(weights)
@@ -701,7 +700,7 @@ modularity.communities <- function(x, ...) {
   if (!is.null(x$modularity)) {
     max(x$modularity)
   } else {
-    stop("Modularity was not calculated")
+    cli::cli_abort("cluster algorithm was run with {.arg modularity = FALSE} and no modularity value was computed.")
   }
 }
 
@@ -761,7 +760,7 @@ merges <- function(communities) {
   if (!is.null(communities$merges)) {
     communities$merges
   } else {
-    stop("Not a hierarchical community structure")
+    cli::cli_abort("Not a hierarchical community structure")
   }
 }
 
@@ -795,10 +794,9 @@ complete.dend <- function(comm, use.modularity) {
   merges <- comm$merges
   if (nrow(merges) < comm$vcount - 1) {
     if (use.modularity) {
-      stop(paste(
-        "`use.modularity' requires a full dendrogram,",
-        "i.e. a connected graph"
-      ))
+      cli::cli_abort(
+        "{.arg use.modularity} requires a full dendrogram, i.e. a connected graph"
+      )
     }
     miss <- seq_len(comm$vcount + nrow(merges))[-as.vector(merges)]
     miss <- c(miss, seq_len(length(miss) - 2) + comm$vcount + nrow(merges))
@@ -819,7 +817,7 @@ complete.dend <- function(comm, use.modularity) {
 as.dendrogram.communities <- function(object, hang = -1, use.modularity = FALSE,
                                       ...) {
   if (!is_hierarchical(object)) {
-    stop("Not a hierarchical community structure")
+    cli::cli_abort("Not a hierarchical community structure")
   }
 
   .memberDend <- function(x) {
@@ -848,7 +846,7 @@ as.dendrogram.communities <- function(object, hang = -1, use.modularity = FALSE,
   }
   nMerge <- length(oHgt <- object$height)
   if (nMerge != nrow(merges)) {
-    stop("'merge' and 'height' do not fit!")
+    cli::cli_abort("'merge' and 'height' do not fit!")
   }
   hMax <- oHgt[nMerge]
   one <- 1L
@@ -919,7 +917,7 @@ as.hclust.communities <- function(x, hang = -1, use.modularity = FALSE,
 
 as.phylo.communities <- function(x, use.modularity = FALSE, ...) {
   if (!is_hierarchical(x)) {
-    stop("Not a hierarchical community structure")
+    cli::cli_abort("Not a hierarchical community structure")
   }
 
   ## If multiple components, then we merge them in arbitrary order
@@ -974,15 +972,15 @@ rlang::on_load(s3_register("ape::as.phylo", "communities"))
 #' @export
 cut_at <- function(communities, no, steps) {
   if (!inherits(communities, "communities")) {
-    stop("Not a community structure")
+    cli::cli_abort("Not a community structure")
   }
   if (!is_hierarchical(communities)) {
-    stop("Not a hierarchical communitity structure")
+    cli::cli_abort("Not a hierarchical communitity structure")
   }
 
   if ((!missing(no) && !missing(steps)) ||
     (missing(no) && missing(steps))) {
-    stop("Please give either `no' or `steps' (but not both)")
+    cli::cli_abort("Please use either {.arg no} or {.arg steps} (but not both)")
   }
 
   if (!missing(steps)) {
@@ -1008,10 +1006,10 @@ cut_at <- function(communities, no, steps) {
 #' @export
 show_trace <- function(communities) {
   if (!inherits(communities, "communities")) {
-    stop("Not a community structure")
+    cli::cli_abort("Not a community structure")
   }
   if (is.null(communities$history)) {
-    stop("History was not recorded")
+    cli::cli_abort("History was not recorded")
   }
 
   res <- character()
@@ -1340,13 +1338,14 @@ cluster_leiden <- function(graph, objective_function = c("CPM", "modularity"),
                            resolution_parameter = deprecated(), beta = 0.01,
                            initial_membership = NULL,
                            n_iterations = 2, vertex_weights = NULL) {
-
   check_dots_empty()
 
   if (lifecycle::is_present(resolution_parameter)) {
-    lifecycle::deprecate_soft("2.1.0",
-                              "cluster_leiden(resolution_parameter)",
-                              "cluster_leiden(resolution)")
+    lifecycle::deprecate_soft(
+      "2.1.0",
+      "cluster_leiden(resolution_parameter)",
+      "cluster_leiden(resolution)"
+    )
     resolution <- resolution_parameter
   }
 
@@ -1879,7 +1878,6 @@ cluster_leading_eigen <- function(graph, steps = -1, weights = NULL,
                                   options = arpack_defaults(),
                                   callback = NULL, extra = NULL,
                                   env = parent.frame()) {
-
   if (is.function(options)) {
     lifecycle::deprecate_soft(
       "1.6.0",
@@ -2041,8 +2039,16 @@ cluster_label_prop0 <- function(
   if (!is.null(initial)) initial <- as.numeric(initial)
   if (!is.null(fixed)) fixed <- as.logical(fixed)
 
-  directed <- switch(igraph.match.arg(mode), "out" = TRUE, "in" = TRUE, "all" = FALSE)
-  mode <- switch(igraph.match.arg(mode), "out" = 1L, "in" = 2L, "all" = 3L)
+  directed <- switch(igraph.match.arg(mode),
+    "out" = TRUE,
+    "in" = TRUE,
+    "all" = FALSE
+  )
+  mode <- switch(igraph.match.arg(mode),
+    "out" = 1L,
+    "in" = 2L,
+    "all" = 3L
+  )
 
   on.exit(.Call(R_igraph_finalizer))
   # Function call
@@ -2828,10 +2834,10 @@ contract <- contract_vertices_impl
 #' @seealso [distances()]
 #' @examples
 #'
-#' g <- make_lattice(c(10,10))
+#' g <- make_lattice(c(10, 10))
 #' clu <- voronoi_cells(g, c(25, 43, 67))
 #' groups(clu)
-#' plot(g, vertex.color=clu$membership)
+#' plot(g, vertex.color = clu$membership)
 #'
 #' @export
 #' @family community
diff --git a/tests/testthat/_snaps/modularity_matrix.md b/tests/testthat/_snaps/community.md
similarity index 73%
rename from tests/testthat/_snaps/modularity_matrix.md
rename to tests/testthat/_snaps/community.md
index 3af711323fb..e01280a95f5 100644
--- a/tests/testthat/_snaps/modularity_matrix.md
+++ b/tests/testthat/_snaps/community.md
@@ -1,7 +1,7 @@
 # modularity_matrix still accepts a membership argument for compatibility
 
     Code
-      x <- modularity_matrix(kar, membership = rep(1, vcount(kar)))
+      x <- modularity_matrix(karate, membership = rep(1, vcount(karate)))
     Condition
       Warning:
       The `membership` argument of `modularity_matrix()` is no longer used as of igraph 2.1.0.
diff --git a/tests/testthat/test-communities.R b/tests/testthat/test-communities.R
deleted file mode 100644
index 4301d339263..00000000000
--- a/tests/testthat/test-communities.R
+++ /dev/null
@@ -1,74 +0,0 @@
-test_that("community detection functions work", {
-  withr::local_seed(42)
-
-  F <- list(
-    "cluster_edge_betweenness", "cluster_fast_greedy",
-    "cluster_label_prop", "cluster_leading_eigen",
-    "cluster_louvain", "cluster_spinglass", "cluster_walktrap"
-  )
-  if (has_glpk()) F <- c(F, list("cluster_optimal"))
-
-  karate <- make_graph("Zachary")
-
-  for (f in F) {
-    f <- get(f)
-    comm <- f(karate)
-
-    expect_equal(
-      modularity(comm),
-      modularity(karate, membership(comm))
-    )
-
-    cc <- communities(comm)
-    expect_true(all(!duplicated(unlist(cc))))
-    expect_true(all(unlist(cc) <= vcount(karate) & unlist(cc) >= 1))
-    expect_equal(length(comm), max(membership(comm)))
-  }
-
-  fc <- cluster_fast_greedy(karate)
-  m1 <- modularity(karate, cut_at(fc, no = 1))
-  m2 <- modularity(karate, cut_at(fc, no = 2))
-  m3 <- modularity(karate, cut_at(fc, no = 3))
-  m4 <- modularity(karate, cut_at(fc, no = 4))
-  expect_equal(m1, 0)
-  expect_equal(m2, 0.3717948718)
-  expect_equal(m3, 0.3806706114)
-  expect_equal(m4, 0.3759861933)
-
-  cr <- crossing(fc, karate)
-  expect_equal(cr, c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE))
-})
-
-test_that("creating communities objects works", {
-  withr::local_seed(42)
-
-  karate <- make_graph("Zachary")
-
-  membership <- sample(1:2, vcount(karate), replace = TRUE)
-  mod <- modularity(karate, membership)
-  comm <- make_clusters(
-    algorithm = "random", membership = membership,
-    modularity = mod
-  )
-
-  expect_equal(as.vector(membership(comm)), membership)
-  expect_equal(modularity(comm), mod)
-  expect_equal(algorithm(comm), "random")
-})
-
-test_that("communities function works", {
-  skip_if_no_glpk()
-  g <- make_graph("Zachary")
-  oc <- cluster_optimal(g)
-  gr <- communities(oc)
-  expect_equal(
-    gr,
-    structure(list(`1` = c(1L, 2L, 3L, 4L, 8L, 12L, 13L, 14L, 18L, 20L, 22L), `2` = c(5L, 6L, 7L, 11L, 17L), `3` = c(9L, 10L, 15L, 16L, 19L, 21L, 23L, 27L, 30L, 31L, 33L, 34L), `4` = c(24L, 25L, 26L, 28L, 29L, 32L)), .Dim = 4L, .Dimnames = list(c("1", "2", "3", "4")))
-  )
-
-  g <- make_ring(5) + make_ring(5)
-  V(g)$name <- letters[1:10]
-  oc <- cluster_optimal(g)
-  gr <- communities(oc)
-  expect_equal(gr, structure(list(`1` = letters[1:5], `2` = letters[6:10]), .Dim = 2L, .Dimnames = list(c("1", "2"))))
-})
diff --git a/tests/testthat/test-community.R b/tests/testthat/test-community.R
new file mode 100644
index 00000000000..69859330b1d
--- /dev/null
+++ b/tests/testthat/test-community.R
@@ -0,0 +1,437 @@
+test_that("community detection functions work", {
+  withr::local_seed(42)
+
+  cluster_algos <- list(
+    "cluster_edge_betweenness", "cluster_fast_greedy",
+    "cluster_label_prop", "cluster_leading_eigen",
+    "cluster_louvain", "cluster_spinglass", "cluster_walktrap"
+  )
+  if (has_glpk()) cluster_algos <- c(cluster_algos, "cluster_optimal")
+
+  karate <- make_graph("Zachary")
+
+  for (algo in cluster_algos) {
+    karate_clustering <- do.call(algo, list(karate))
+
+    expect_equal(
+      modularity(karate_clustering),
+      modularity(karate, membership(karate_clustering))
+    )
+
+    karate_comunities <- communities(karate_clustering)
+    flat_karate_communities <- unlist(karate_comunities)
+    is_vertex_in_several_clusters <- duplicated(flat_karate_communities)
+    expect_false(any(is_vertex_in_several_clusters))
+    is_cluster_id_valid <- flat_karate_communities <= vcount(karate) & flat_karate_communities >= 1
+    expect_true(all(is_cluster_id_valid))
+    expect_length(karate_clustering, max(membership(karate_clustering)))
+  }
+
+  karate_fgreedy <- cluster_fast_greedy(karate)
+  m1 <- modularity(karate, cut_at(karate_fgreedy, no = 1))
+  expect_equal(m1, 0)
+
+  m2 <- modularity(karate, cut_at(karate_fgreedy, no = 2))
+  expect_equal(m2, 0.3717948718)
+
+  m3 <- modularity(karate, cut_at(karate_fgreedy, no = 3))
+  expect_equal(m3, 0.3806706114)
+
+  m4 <- modularity(karate, cut_at(karate_fgreedy, no = 4))
+  expect_equal(m4, 0.3759861933)
+
+  cr <- crossing(karate_fgreedy, karate)
+  expect_equal(
+    cr,
+    c(
+      TRUE, TRUE, TRUE, FALSE, FALSE,
+      FALSE, TRUE, TRUE, FALSE, FALSE,
+      TRUE, TRUE, TRUE, FALSE, TRUE, TRUE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,
+      FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, TRUE,
+      FALSE, TRUE, FALSE, FALSE, FALSE, FALSE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
+      FALSE, FALSE, FALSE, TRUE, TRUE, FALSE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, TRUE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, FALSE,
+      FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE
+    )
+  )
+})
+
+test_that("creating communities objects works", {
+  withr::local_seed(42)
+
+  karate <- make_graph("Zachary")
+
+  membership <- sample(1:2, vcount(karate), replace = TRUE)
+  mod <- modularity(karate, membership)
+  comm <- make_clusters(
+    algorithm = "random", membership = membership,
+    modularity = mod
+  )
+
+  expect_equal(membership(comm), membership)
+  expect_equal(modularity(comm), mod)
+  expect_equal(algorithm(comm), "random")
+})
+
+test_that("communities function works", {
+  skip_if_no_glpk()
+  karate <- make_graph("Zachary")
+  karate_optimal <- cluster_optimal(karate)
+  karate_coms <- communities(karate_optimal)
+  expect_equal(
+    karate_coms,
+    structure(
+      list(
+        `1` = c(1L, 2L, 3L, 4L, 8L, 12L, 13L, 14L, 18L, 20L, 22L),
+        `2` = c(5L, 6L, 7L, 11L, 17L),
+        `3` = c(9L, 10L, 15L, 16L, 19L, 21L, 23L, 27L, 30L, 31L, 33L, 34L),
+        `4` = c(24L, 25L, 26L, 28L, 29L, 32L)
+      ),
+      .Dim = 4L, .Dimnames = list(c("1", "2", "3", "4"))
+    )
+  )
+
+  double_ring <- make_ring(5) + make_ring(5)
+  V(double_ring)$name <- letters[1:10]
+  double_ring_optimal <- cluster_optimal(double_ring)
+  double_ring_coms <- communities(double_ring_optimal)
+  expect_equal(
+    double_ring_coms,
+    structure(
+      list(
+        `1` = letters[1:5],
+        `2` = letters[6:10]
+      ),
+      .Dim = 2L, .Dimnames = list(c("1", "2"))
+    )
+  )
+})
+
+test_that("cluster_edge_betweenness works", {
+  karate <- make_graph("Zachary")
+  karate_ebc <- cluster_edge_betweenness(karate)
+
+  expect_equal(max(karate_ebc$modularity), modularity(karate, karate_ebc$membership))
+  expect_equal(
+    membership(karate_ebc),
+    c(
+      1, 1, 2, 1, 3, 3, 3, 1, 4, 5, 3,
+      1, 1, 1, 4, 4, 3, 1, 4, 1, 4, 1,
+      4, 4, 2, 2, 4, 2, 2, 4, 4, 2, 4, 4
+    )
+  )
+  expect_length(karate_ebc, 5)
+  expect_equal(as.numeric(sizes(karate_ebc)), c(10, 6, 5, 12, 1))
+
+  karate_dendro <- as.dendrogram(karate_ebc)
+  expect_output(print(karate_dendro), "2 branches.*34 members.*height 33")
+  expect_output(
+    print(karate_dendro[[1]]),
+    "2 branches.*15 members.*height 31"
+  )
+  expect_output(
+    print(karate_dendro[[2]]),
+    "2 branches.*19 members.*height 32"
+  )
+  m2 <- cut_at(karate_ebc, no = 3)
+  expect_equal(
+    modularity(karate, m2),
+    karate_ebc$modularity[length(karate_ebc$modularity) - 2]
+  )
+})
+
+test_that("cluster_fast_greedy works", {
+  withr::local_seed(42)
+
+  karate <- make_graph("Zachary")
+  karate_fc <- cluster_fast_greedy(karate)
+
+  expect_equal(modularity(karate, karate_fc$membership), max(karate_fc$modularity))
+  expect_equal(
+    membership(karate_fc),
+    c(
+      1, 3, 3, 3, 1, 1, 1, 3, 2, 3, 1,
+      1, 3, 3, 2, 2, 1, 3, 2, 1, 2, 3,
+      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+    )
+  )
+  expect_length(karate_fc, 3)
+  expect_equal(as.numeric(sizes(karate_fc)), c(8, 17, 9))
+
+  karate_dendro <- as.dendrogram(karate_fc)
+  expect_output(print(karate_dendro), "2 branches.*34 members.*height 33")
+  expect_output(print(karate_dendro[[1]]), "2 branches.*17 members.*height 32")
+  expect_output(print(karate_dendro[[2]]), "2 branches.*17 members.*height 30")
+  m2 <- cut_at(karate_fc, no = 3)
+  expect_equal(
+    modularity(karate, m2),
+    karate_fc$modularity[length(karate_fc$modularity) - 2]
+  )
+})
+
+test_that("label.propagation.community works", {
+  karate <- make_graph("Zachary")
+  withr::local_seed(20231029)
+  karate_lpc <- cluster_label_prop(karate)
+  expect_equal(karate_lpc$modularity, modularity(karate, karate_lpc$membership))
+
+  expect_in(length(karate_lpc), 1:5)
+  expect_in(membership(karate_lpc), seq_len(length(karate_lpc)))
+  expect_s3_class(sizes(karate_lpc), "table")
+  expect_equal(sum(sizes(karate_lpc)), vcount(karate))
+  expect_identical(sizes(karate_lpc), table(membership(karate_lpc), dnn = "Community sizes"))
+})
+
+test_that("cluster_leading_eigen works", {
+  withr::local_seed(20230115)
+
+  check_eigen_value <- function(membership, community, value, vector, multiplier, extra) {
+    M <- sapply(1:length(vector), function(x) {
+      v <- rep(0, length(vector))
+      v[x] <- 1
+      multiplier(v)
+    })
+    ev <- eigen(M)
+    ret <- 0
+    expect_equal(ev$values[1], value)
+    if (sign(ev$vectors[1, 1]) != sign(vector[1])) {
+      ev$vectors <- -ev$vectors
+    }
+    expect_equal(ev$vectors[, 1], vector)
+    0
+  }
+
+  karate <- make_graph("Zachary")
+  karate_lc <- cluster_leading_eigen(karate, callback = check_eigen_value)
+
+  expect_equal(karate_lc$modularity, modularity(karate, karate_lc$membership))
+  expect_equal(
+    membership(karate_lc),
+    c(
+      1, 3, 3, 3, 1, 1, 1, 3, 2, 2, 1,
+      1, 3, 3, 2, 2, 1, 3, 2, 3, 2, 3,
+      2, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 2
+    )
+  )
+  expect_length(karate_lc, 4)
+  expect_equal(
+    sizes(karate_lc),
+    structure(
+      c(7L, 12L, 9L, 6L),
+      .Dim = 4L,
+      .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")),
+        .Names = "Community sizes"
+      ), class = "table"
+    )
+  )
+
+  ## Check that the modularity matrix is correct
+
+  mod_mat_caller <- function(membership, community, value, vector, multiplier, extra) {
+    M <- sapply(1:length(vector), function(x) {
+      v <- rep(0, length(vector))
+      v[x] <- 1
+      multiplier(v)
+    })
+    myc <- membership == community
+    B <- A[myc, myc] - (deg[myc] %*% t(deg[myc])) / 2 / ec
+    BG <- B - diag(rowSums(B))
+
+    expect_equal(M, BG)
+    0
+  }
+
+  A <- as_adjacency_matrix(karate, sparse = FALSE)
+  ec <- ecount(karate)
+  deg <- degree(karate)
+  karate_lc2 <- cluster_leading_eigen(karate, callback = mod_mat_caller)
+})
+test_that("cluster_leading_eigen is deterministic", {
+  ## Stress-test. We skip this on R 3.4 and 3.5 because it seems like
+  ## the results are not entirely deterministic there.
+
+  skip_if(getRversion() < "3.6")
+
+  for (i in 1:100) {
+    g_rand <- sample_gnm(20, sample(5:40, 1))
+    lec1 <- cluster_leading_eigen(g_rand)
+    lec2 <- cluster_leading_eigen(g_rand)
+    expect_equal(
+      membership(lec1),
+      membership(lec2)
+    )
+  }
+})
+
+test_that("cluster_leiden works", {
+  withr::local_seed(42)
+
+  karate <- make_graph("Zachary")
+  karate_leiden <- cluster_leiden(karate, resolution = 0.06)
+
+  expect_equal(
+    membership(karate_leiden),
+    c(
+      1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1,
+      1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1,
+      2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
+    )
+  )
+  expect_length(karate_leiden, 2)
+  expect_equal(
+    sizes(karate_leiden),
+    structure(
+      c(17L, 17L),
+      .Dim = 2L,
+      .Dimnames = structure(list(`Community sizes` = c("1", "2")),
+        .Names = "Community sizes"
+      ), class = "table"
+    )
+  )
+
+  karate_leiden_mod <- cluster_leiden(karate, "modularity")
+
+  expect_equal(
+    membership(karate_leiden_mod),
+    c(
+      1, 1, 1, 1, 2, 2, 2, 1, 3, 3, 2, 1, 1,
+      1, 3, 3, 2, 1, 3, 1, 3, 1, 3, 4, 4, 4,
+      3, 4, 4, 3, 3, 4, 3, 3
+    )
+  )
+  expect_length(karate_leiden_mod, 4)
+  expect_equal(
+    sizes(karate_leiden_mod),
+    structure(
+      c(11L, 5L, 12L, 6L),
+      .Dim = 4L,
+      .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")),
+        .Names = "Community sizes"
+      ), class = "table"
+    )
+  )
+})
+
+test_that("modularity_matrix works", {
+  karate <- make_graph("zachary")
+
+  karate_fc <- cluster_fast_greedy(karate)
+
+  karate_m1 <- modularity(karate, membership(karate_fc))
+  karate_m2 <- modularity(karate, membership(karate_fc), weights = rep(1, ecount(karate)))
+  expect_equal(karate_m1, karate_m2)
+
+  karate_modmat1 <- modularity_matrix(karate)
+  karate_modmat2 <- modularity_matrix(karate, weights = rep(1, ecount(karate)))
+
+  expect_equal(karate_modmat1, karate_modmat2)
+})
+
+test_that("modularity_matrix still accepts a membership argument for compatibility", {
+  karate <- make_graph("zachary")
+  expect_snapshot(
+    x <- modularity_matrix(karate, membership = rep(1, vcount(karate)))
+  )
+})
+
+test_that("cluster_louvain works", {
+  withr::local_seed(20231029)
+
+  karate <- make_graph("Zachary")
+  karate_mc <- cluster_louvain(karate)
+
+  expect_in(membership(karate_mc), 1:4)
+  expect_equal(modularity(karate, karate_mc$membership), max(karate_mc$modularity))
+  expect_in(length(karate_mc), 3:4)
+  expect_in(membership(karate_mc), seq_len(length(karate_mc)))
+  expect_s3_class(sizes(karate_mc), "table")
+  expect_equal(sum(sizes(karate_mc)), vcount(karate))
+  expect_identical(sizes(karate_mc), table(membership(karate_mc), dnn = "Community sizes"))
+})
+
+test_that("cluster_optimal works", {
+  skip_if_no_glpk()
+
+  karate <- make_graph("Zachary")
+  karate_optimal <- cluster_optimal(karate)
+
+  expect_equal(
+    membership(karate_optimal),
+    c(
+      1, 1, 1, 1, 2, 2, 2, 1, 3, 3, 2,
+      1, 1, 1, 3, 3, 2, 1, 3, 1, 3, 1,
+      3, 4, 4, 4, 3, 4, 4, 3, 3, 4, 3, 3
+    )
+  )
+  expect_equal(modularity(karate, karate_optimal$membership), karate_optimal$modularity)
+  expect_length(karate_optimal, 4)
+  expect_equal(
+    sizes(karate_optimal),
+    structure(
+      c(11L, 5L, 12L, 6L),
+      .Dim = 4L,
+      .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")),
+        .Names = "Community sizes"
+      ), class = "table"
+    )
+  )
+})
+
+test_that("weighted cluster_optimal works", {
+  skip_if_no_glpk()
+  local_rng_version("3.5.0")
+  withr::local_seed(42)
+
+  graph_full_ring <- make_full_graph(5) + make_ring(5)
+  E(graph_full_ring)$weight <- sample(1:2, ecount(graph_full_ring), replace = TRUE)
+
+  graph_full_ring_optimal <- cluster_optimal(graph_full_ring)
+  expect_equal(modularity(graph_full_ring_optimal), 0.4032)
+})
+
+test_that("cluster_walktrap works", {
+  withr::local_seed(42)
+
+  karate <- make_graph("Zachary")
+  karate_walktrap <- cluster_walktrap(karate)
+
+  expect_equal(modularity(karate, membership(karate_walktrap)), modularity(karate_walktrap))
+  expect_equal(
+    membership(karate_walktrap),
+    c(1, 1, 2, 1, 5, 5, 5, 1, 2, 2, 5, 1, 1, 2, 3, 3, 5, 1, 3, 1, 3, 1, 3, 4, 4, 4, 3, 4, 2, 3, 2, 2, 3, 3)
+  )
+  expect_length(karate_walktrap, 5)
+  expect_equal(
+    sizes(karate_walktrap),
+    structure(c(9L, 7L, 9L, 4L, 5L),
+      .Dim = 5L,
+      .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4", "5")), .Names = "Community sizes"), class = "table"
+    )
+  )
+
+  karate_dendro <- as.dendrogram(karate_walktrap)
+  expect_output(print(karate_dendro), "2 branches.*34 members.*height 33")
+  expect_output(print(karate_dendro[[1]]), "2 branches.*20 members.*height 31")
+  expect_output(print(karate_dendro[[2]]), "2 branches.*14 members.*height 32")
+  m2 <- cut_at(karate_walktrap, no = 3)
+  expect_equal(
+    modularity(karate, m2),
+    karate_walktrap$modularity[length(karate_walktrap$modularity) - 2]
+  )
+})
+
+test_that("split_join_distance works", {
+  random_sjd <- unname(split_join_distance(rep(1:2, each = 17), rep(1, 34)))
+  expect_equal(random_sjd, c(0, 17))
+
+  karate <- make_graph("Zachary")
+  karate_split1 <- make_clusters(karate, rep(1:2, each = 17))
+  karate_split2 <- make_clusters(karate, rep(1, 34))
+  com_sjd <- unname(split_join_distance(karate_split1, karate_split2))
+  expect_equal(com_sjd, c(0, 17))
+})
diff --git a/tests/testthat/test-edge.betweenness.community.R b/tests/testthat/test-edge.betweenness.community.R
deleted file mode 100644
index 09270468a7b..00000000000
--- a/tests/testthat/test-edge.betweenness.community.R
+++ /dev/null
@@ -1,28 +0,0 @@
-test_that("cluster_edge_betweenness works", {
-  g <- make_graph("Zachary")
-  ebc <- cluster_edge_betweenness(g)
-
-  expect_equal(max(ebc$modularity), modularity(g, ebc$membership))
-  expect_equal(
-    as.vector(membership(ebc)),
-    c(1, 1, 2, 1, 3, 3, 3, 1, 4, 5, 3, 1, 1, 1, 4, 4, 3, 1, 4, 1, 4, 1, 4, 4, 2, 2, 4, 2, 2, 4, 4, 2, 4, 4)
-  )
-  expect_equal(length(ebc), 5)
-  expect_equal(as.numeric(sizes(ebc)), c(10, 6, 5, 12, 1))
-
-  d <- as.dendrogram(ebc)
-  expect_output(print(d), "2 branches.*34 members.*height 33")
-  expect_output(
-    print(d[[1]]),
-    "2 branches.*15 members.*height 31"
-  )
-  expect_output(
-    print(d[[2]]),
-    "2 branches.*19 members.*height 32"
-  )
-  m2 <- cut_at(ebc, no = 3)
-  expect_equal(
-    modularity(g, m2),
-    ebc$modularity[length(ebc$modularity) - 2]
-  )
-})
diff --git a/tests/testthat/test-fastgreedy.community.R b/tests/testthat/test-fastgreedy.community.R
deleted file mode 100644
index 51e5aab42f3..00000000000
--- a/tests/testthat/test-fastgreedy.community.R
+++ /dev/null
@@ -1,24 +0,0 @@
-test_that("cluster_fast_greedy works", {
-  withr::local_seed(42)
-
-  g <- make_graph("Zachary")
-  fc <- cluster_fast_greedy(g)
-
-  expect_equal(modularity(g, fc$membership), max(fc$modularity))
-  expect_equal(
-    as.vector(membership(fc)),
-    c(1, 3, 3, 3, 1, 1, 1, 3, 2, 3, 1, 1, 3, 3, 2, 2, 1, 3, 2, 1, 2, 3, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
-  )
-  expect_equal(length(fc), 3)
-  expect_equal(as.numeric(sizes(fc)), c(8, 17, 9))
-
-  d <- as.dendrogram(fc)
-  expect_output(print(d), "2 branches.*34 members.*height 33")
-  expect_output(print(d[[1]]), "2 branches.*17 members.*height 32")
-  expect_output(print(d[[2]]), "2 branches.*17 members.*height 30")
-  m2 <- cut_at(fc, no = 3)
-  expect_equal(
-    modularity(g, m2),
-    fc$modularity[length(fc$modularity) - 2]
-  )
-})
diff --git a/tests/testthat/test-label.propagation.community.R b/tests/testthat/test-label.propagation.community.R
deleted file mode 100644
index 5953c33bc91..00000000000
--- a/tests/testthat/test-label.propagation.community.R
+++ /dev/null
@@ -1,13 +0,0 @@
-test_that("label.propagation.community works", {
-  g <- make_graph("Zachary")
-  withr::local_seed(20231029)
-  lpc <- cluster_label_prop(g)
-  expect_equal(lpc$modularity, modularity(g, lpc$membership))
-  #  1   2   3   4   5
-  # 29 453 431  84   3
-  expect_true(length(lpc) %in% 1:5)
-  expect_true(all(as.vector(membership(lpc)) %in% seq_len(length(lpc))))
-  expect_s3_class(sizes(lpc), "table")
-  expect_equal(sum(sizes(lpc)), vcount(g))
-  expect_identical(sizes(lpc), table(membership(lpc), dnn = "Community sizes"))
-})
diff --git a/tests/testthat/test-leading.eigenvector.community.R b/tests/testthat/test-leading.eigenvector.community.R
deleted file mode 100644
index 9aadd9be082..00000000000
--- a/tests/testthat/test-leading.eigenvector.community.R
+++ /dev/null
@@ -1,71 +0,0 @@
-test_that("cluster_leading_eigen works", {
-  withr::local_seed(20230115)
-
-  ## Check-test
-
-  f <- function(membership, community, value, vector, multiplier, extra) {
-    M <- sapply(1:length(vector), function(x) {
-      v <- rep(0, length(vector))
-      v[x] <- 1
-      multiplier(v)
-    })
-    ev <- eigen(M)
-    ret <- 0
-    expect_equal(ev$values[1], value)
-    if (sign(ev$vectors[1, 1]) != sign(vector[1])) {
-      ev$vectors <- -ev$vectors
-    }
-    expect_equal(ev$vectors[, 1], vector)
-    0
-  }
-
-  g <- make_graph("Zachary")
-  lc <- cluster_leading_eigen(g, callback = f)
-
-  expect_equal(lc$modularity, modularity(g, lc$membership))
-  expect_equal(
-    as.vector(membership(lc)),
-    c(1, 3, 3, 3, 1, 1, 1, 3, 2, 2, 1, 1, 3, 3, 2, 2, 1, 3, 2, 3, 2, 3, 2, 4, 4, 4, 2, 4, 4, 2, 2, 4, 2, 2)
-  )
-  expect_equal(length(lc), 4)
-  expect_equal(
-    sizes(lc),
-    structure(c(7L, 12L, 9L, 6L), .Dim = 4L, .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")), .Names = "Community sizes"), class = "table")
-  )
-
-  ## Check that the modularity matrix is correct
-
-  f <- function(membership, community, value, vector, multiplier, extra) {
-    M <- sapply(1:length(vector), function(x) {
-      v <- rep(0, length(vector))
-      v[x] <- 1
-      multiplier(v)
-    })
-    myc <- membership == community
-    B <- A[myc, myc] - (deg[myc] %*% t(deg[myc])) / 2 / ec
-    BG <- B - diag(rowSums(B))
-
-    expect_equal(M, BG)
-    0
-  }
-
-  g <- make_graph("Zachary")
-  A <- as_adjacency_matrix(g, sparse = FALSE)
-  ec <- ecount(g)
-  deg <- degree(g)
-  lc <- cluster_leading_eigen(g, callback = f)
-
-  ## Stress-test. We skip this on R 3.4 and 3.5 because it seems like
-  ## the results are not entirely deterministic there.
-  skip_if(getRversion() < "3.6")
-
-  for (i in 1:100) {
-    g <- sample_gnm(20, sample(5:40, 1))
-    lec1 <- cluster_leading_eigen(g)
-    lec2 <- cluster_leading_eigen(g)
-    expect_equal(
-      as.vector(membership(lec1)),
-      as.vector(membership(lec2))
-    )
-  }
-})
diff --git a/tests/testthat/test-leiden.R b/tests/testthat/test-leiden.R
deleted file mode 100644
index 204377254c3..00000000000
--- a/tests/testthat/test-leiden.R
+++ /dev/null
@@ -1,29 +0,0 @@
-test_that("cluster_leiden works", {
-  withr::local_seed(42)
-
-  g <- make_graph("Zachary")
-  mc <- cluster_leiden(g, resolution = 0.06)
-
-  expect_equal(
-    as.vector(membership(mc)),
-    c(1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 2, 2, 1, 1, 2, 1, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2)
-  )
-  expect_equal(length(mc), 2)
-  expect_equal(
-    sizes(mc),
-    structure(c(17L, 17L), .Dim = 2L, .Dimnames = structure(list(`Community sizes` = c("1", "2")), .Names = "Community sizes"), class = "table")
-  )
-
-  withr::local_seed(42)
-  mc <- cluster_leiden(g, "modularity")
-
-  expect_equal(
-    as.vector(membership(mc)),
-    c(1, 1, 1, 1, 2, 2, 2, 1, 3, 3, 2, 1, 1, 1, 3, 3, 2, 1, 3, 1, 3, 1, 3, 4, 4, 4, 3, 4, 4, 3, 3, 4, 3, 3)
-  )
-  expect_equal(length(mc), 4)
-  expect_equal(
-    sizes(mc),
-    structure(c(11L, 5L, 12L, 6L), .Dim = 4L, .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")), .Names = "Community sizes"), class = "table")
-  )
-})
diff --git a/tests/testthat/test-modularity_matrix.R b/tests/testthat/test-modularity_matrix.R
deleted file mode 100644
index cf7dc5ea660..00000000000
--- a/tests/testthat/test-modularity_matrix.R
+++ /dev/null
@@ -1,21 +0,0 @@
-test_that("modularity_matrix works", {
-  kar <- make_graph("zachary")
-
-  fc <- cluster_fast_greedy(kar)
-
-  m1 <- modularity(kar, membership(fc))
-  m2 <- modularity(kar, membership(fc), weights = rep(1, ecount(kar)))
-  expect_equal(m1, m2)
-
-  B1 <- modularity_matrix(kar)
-  B2 <- modularity_matrix(kar, weights = rep(1, ecount(kar)))
-
-  expect_equal(B1, B2)
-})
-
-test_that("modularity_matrix still accepts a membership argument for compatibility", {
-  kar <- make_graph("zachary")
-  expect_snapshot(
-    x <- modularity_matrix(kar, membership = rep(1, vcount(kar)))
-  )
-})
diff --git a/tests/testthat/test-multilevel.community.R b/tests/testthat/test-multilevel.community.R
deleted file mode 100644
index daea4ee6ad3..00000000000
--- a/tests/testthat/test-multilevel.community.R
+++ /dev/null
@@ -1,16 +0,0 @@
-test_that("cluster_louvain works", {
-  withr::local_seed(20231029)
-
-  g <- make_graph("Zachary")
-  mc <- cluster_louvain(g)
-
-  expect_true(all(as.vector(membership(mc)) %in% 1:4))
-  expect_equal(modularity(g, mc$membership), max(mc$modularity))
-  # 3   4
-  # 2 998
-  expect_true(length(mc) %in% 3:4)
-  expect_true(all(as.vector(membership(mc)) %in% seq_len(length(mc))))
-  expect_s3_class(sizes(mc), "table")
-  expect_equal(sum(sizes(mc)), vcount(g))
-  expect_identical(sizes(mc), table(membership(mc), dnn = "Community sizes"))
-})
diff --git a/tests/testthat/test-optimal.community.R b/tests/testthat/test-optimal.community.R
deleted file mode 100644
index 3516e9ab07c..00000000000
--- a/tests/testthat/test-optimal.community.R
+++ /dev/null
@@ -1,29 +0,0 @@
-test_that("cluster_optimal works", {
-  skip_if_no_glpk()
-
-  g <- make_graph("Zachary")
-  oc <- cluster_optimal(g)
-
-  expect_equal(
-    as.vector(membership(oc)),
-    c(1, 1, 1, 1, 2, 2, 2, 1, 3, 3, 2, 1, 1, 1, 3, 3, 2, 1, 3, 1, 3, 1, 3, 4, 4, 4, 3, 4, 4, 3, 3, 4, 3, 3)
-  )
-  expect_equal(modularity(g, oc$membership), oc$modularity)
-  expect_equal(length(oc), 4)
-  expect_equal(
-    sizes(oc),
-    structure(c(11L, 5L, 12L, 6L), .Dim = 4L, .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4")), .Names = "Community sizes"), class = "table")
-  )
-})
-
-test_that("weighted cluster_optimal works", {
-  skip_if_no_glpk()
-
-  local_rng_version("3.5.0")
-  withr::local_seed(42)
-  g <- make_full_graph(5) + make_ring(5)
-  E(g)$weight <- sample(1:2, ecount(g), replace = TRUE)
-
-  oc <- cluster_optimal(g)
-  expect_equal(modularity(oc), 0.4032)
-})
diff --git a/tests/testthat/test-walktrap.community.R b/tests/testthat/test-walktrap.community.R
deleted file mode 100644
index 57c53eef9d1..00000000000
--- a/tests/testthat/test-walktrap.community.R
+++ /dev/null
@@ -1,23 +0,0 @@
-test_that("cluster_walktrap works", {
-  g <- make_graph("Zachary")
-  withr::local_seed(42)
-  wc <- cluster_walktrap(g)
-
-  expect_equal(modularity(g, membership(wc)), modularity(wc))
-  expect_equal(
-    as.vector(membership(wc)),
-    c(1, 1, 2, 1, 5, 5, 5, 1, 2, 2, 5, 1, 1, 2, 3, 3, 5, 1, 3, 1, 3, 1, 3, 4, 4, 4, 3, 4, 2, 3, 2, 2, 3, 3)
-  )
-  expect_equal(length(wc), 5)
-  expect_equal(sizes(wc), structure(c(9L, 7L, 9L, 4L, 5L), .Dim = 5L, .Dimnames = structure(list(`Community sizes` = c("1", "2", "3", "4", "5")), .Names = "Community sizes"), class = "table"))
-
-  d <- as.dendrogram(wc)
-  expect_output(print(d), "2 branches.*34 members.*height 33")
-  expect_output(print(d[[1]]), "2 branches.*20 members.*height 31")
-  expect_output(print(d[[2]]), "2 branches.*14 members.*height 32")
-  m2 <- cut_at(wc, no = 3)
-  expect_equal(
-    modularity(g, m2),
-    wc$modularity[length(wc$modularity) - 2]
-  )
-})