diff --git a/constclust/clustree.py b/constclust/clustree.py
index 99f9309..e8c338c 100644
--- a/constclust/clustree.py
+++ b/constclust/clustree.py
@@ -74,8 +74,8 @@ def gen_clustree(cluster_df):
         current_nodes = grouped[level]
         next_nodes = grouped[level + 1]
         for current_node, next_node in product(current_nodes, next_nodes):
-            current_contents = g.node[current_node]["contents"]
-            next_contents = g.node[next_node]["contents"]
+            current_contents = g.nodes[current_node]["contents"]
+            next_contents = g.nodes[next_node]["contents"]
             intersect = np.intersect1d(
                 current_contents, next_contents, assume_unique=True
             )
diff --git a/tests/test_clustree.py b/tests/test_clustree.py
new file mode 100644
index 0000000..fd65e07
--- /dev/null
+++ b/tests/test_clustree.py
@@ -0,0 +1,55 @@
+import numpy as np
+import pandas as pd
+import networkx as nx
+
+from constclust.clustree import gen_clustree
+
+import pytest
+
+
+@pytest.mark.parametrize(
+    "clusterings,expected",
+    [
+        (
+            pd.DataFrame(
+                {
+                    "a": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    "b": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                    "c": [0, 0, 0, 1, 1, 2, 2, 2, 2, 2],
+                }
+            ),
+            nx.DiGraph(
+                {
+                    0: {1: {"weight": 0.5}, 2: {"weight": 0.5}},
+                    1: {3: {"weight": 0.6}, 4: {"weight": 0.4}},
+                    2: {6: {"weight": 1.0}},
+                }
+            ),
+        ),
+        (
+            pd.DataFrame(
+                {
+                    "a": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                    "b": [1, 1, 1, 1, 1, 0, 0, 0, 0, 0],
+                    "c": [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+                }
+            ),
+            nx.DiGraph(
+                {
+                    0: {1: {"weight": 0.5}, 2: {"weight": 0.5}},
+                    1: {3: {"weight": 0.5}},
+                    2: {3: {"weight": 0.5}},
+                }
+            ),
+        ),
+    ],
+    ids=["tree", "diamond"],
+)
+def test_clustree_weights(clusterings, expected):
+    import networkx.algorithms.isomorphism as iso
+
+    result = gen_clustree(clusterings)
+
+    assert nx.is_isomorphic(
+        expected, result, edge_match=iso.numerical_edge_match("weight", default=1)
+    )