diff --git a/python/cugraph/cugraph/__init__.py b/python/cugraph/cugraph/__init__.py
index 00c36a950b3..86e8cf4e61c 100644
--- a/python/cugraph/cugraph/__init__.py
+++ b/python/cugraph/cugraph/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -30,11 +30,8 @@
 
 from cugraph.structure import (
     Graph,
-    DiGraph,
     MultiGraph,
-    MultiDiGraph,
     BiPartiteGraph,
-    BiPartiteDiGraph,
     from_edgelist,
     from_cudf_edgelist,
     from_pandas_edgelist,
diff --git a/python/cugraph/cugraph/community/subgraph_extraction.py b/python/cugraph/cugraph/community/subgraph_extraction.py
index e83c1623d48..05d61db4132 100644
--- a/python/cugraph/cugraph/community/subgraph_extraction.py
+++ b/python/cugraph/cugraph/community/subgraph_extraction.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -12,7 +12,7 @@
 # limitations under the License.
 
 import cudf
-
+from cugraph.structure import Graph
 from cugraph.community import subgraph_extraction_wrapper
 from cugraph.utilities import (
     ensure_cugraph_obj_for_nx,
@@ -57,6 +57,7 @@ def subgraph(G, vertices):
     """
 
     G, isNx = ensure_cugraph_obj_for_nx(G)
+    directed = G.is_directed()
 
     if G.renumbered:
         if isinstance(vertices, cudf.DataFrame):
@@ -64,7 +65,7 @@ def subgraph(G, vertices):
         else:
             vertices = G.lookup_internal_vertex_id(vertices)
 
-    result_graph = type(G)()
+    result_graph = Graph(directed=directed)
 
     df = subgraph_extraction_wrapper.subgraph(G, vertices)
     src_names = "src"
diff --git a/python/cugraph/cugraph/components/connectivity.py b/python/cugraph/cugraph/components/connectivity.py
index a99aaacc6c6..394c07e94f1 100644
--- a/python/cugraph/cugraph/components/connectivity.py
+++ b/python/cugraph/cugraph/components/connectivity.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -20,7 +20,7 @@
     is_nx_graph_type,
     cupy_package as cp,
 )
-from cugraph.structure import Graph, DiGraph
+from cugraph.structure import Graph
 from cugraph.components import connectivity_wrapper
 import cudf
 from pylibcugraph import weakly_connected_components as pylibcugraph_wcc
@@ -35,7 +35,7 @@ def _ensure_args(api_name, G, directed, connection, return_labels):
     """
     G_type = type(G)
     # Check for Graph-type inputs and set defaults if unset
-    if (G_type in [Graph, DiGraph]) or is_nx_graph_type(G_type):
+    if (G_type in [Graph]) or is_nx_graph_type(G_type):
         exc_value = "'%s' cannot be specified for a Graph-type input"
         if directed is not None:
             raise TypeError(exc_value % "directed")
@@ -71,7 +71,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
     graph algos in this module, based on input_type.
     return_labels is only used for return values from cupy/scipy input types.
     """
-    if input_type in [Graph, DiGraph]:
+    if input_type in [Graph]:
         return df
 
     elif is_nx_graph_type(input_type):
@@ -88,7 +88,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
         sorted_df = df.sort_values("vertex")
         if return_labels:
             if is_cp_matrix_type(input_type):
-                labels = cp.fromDlpack(sorted_df["labels"].to_dlpack())
+                labels = cp.from_dlpack(sorted_df["labels"].to_dlpack())
             else:
                 labels = sorted_df["labels"].to_numpy()
             return (n_components, labels)
@@ -121,7 +121,7 @@ def weakly_connected_components(G, directed=None, connection=None, return_labels
             For non-Graph-type (eg. sparse matrix) values of G only.
             Raises TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or
@@ -231,7 +231,7 @@ def strongly_connected_components(
             For non-Graph-type (eg. sparse matrix) values of G only.
             Raises TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or
@@ -323,7 +323,7 @@ def connected_components(G, directed=None, connection="weak", return_labels=None
             For non-Graph-type (eg. sparse matrix) values of G only. Raises
             TypeError if used with a Graph object.
 
-        If True, then convert the input matrix to a cugraph.DiGraph
+        If True, then convert the input matrix to a Graph(directed=True)
         and only move from point i to point j along paths csgraph[i, j]. If
         False, then find the shortest path on an undirected graph: the
         algorithm can progress from point i to j along csgraph[i, j] or
diff --git a/python/cugraph/cugraph/sampling/node2vec.py b/python/cugraph/cugraph/sampling/node2vec.py
index 5d6e76c05d5..2f94fa9dceb 100644
--- a/python/cugraph/cugraph/sampling/node2vec.py
+++ b/python/cugraph/cugraph/sampling/node2vec.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2022, NVIDIA CORPORATION.
+# Copyright (c) 2022-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -38,7 +38,7 @@ def node2vec(G, start_vertices, max_depth=1, compress_result=True, p=1.0, q=1.0)
     Parameters
     ----------
     G : cuGraph.Graph or networkx.Graph
-        The graph can be either directed (DiGraph) or undirected (Graph).
+        The graph can be either directed or undirected.
         Weights in the graph are ignored.
 
     start_vertices: int or list or cudf.Series or cudf.DataFrame
diff --git a/python/cugraph/cugraph/structure/__init__.py b/python/cugraph/cugraph/structure/__init__.py
index 7f6aa23eadc..a3a64432ded 100644
--- a/python/cugraph/cugraph/structure/__init__.py
+++ b/python/cugraph/cugraph/structure/__init__.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -13,11 +13,8 @@
 
 from cugraph.structure.graph_classes import (
     Graph,
-    DiGraph,
     MultiGraph,
-    MultiDiGraph,
     BiPartiteGraph,
-    BiPartiteDiGraph,
 )
 from cugraph.structure.graph_classes import (
     is_weighted,
diff --git a/python/cugraph/cugraph/structure/convert_matrix.py b/python/cugraph/cugraph/structure/convert_matrix.py
index afd26b9d069..1b46f7db970 100644
--- a/python/cugraph/cugraph/structure/convert_matrix.py
+++ b/python/cugraph/cugraph/structure/convert_matrix.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -18,7 +18,7 @@
 import cudf
 import dask_cudf
 
-from cugraph.structure.graph_classes import DiGraph, Graph
+from cugraph.structure.graph_classes import Graph
 
 # optional dependencies used for handling different input types
 try:
@@ -91,8 +91,6 @@ def from_edgelist(
         elif isinstance(create_using, Graph):
             attrs = {"directed": create_using.is_directed()}
             G = type(create_using)(**attrs)
-        elif isinstance(create_using, DiGraph):
-            G = type(create_using)()
         elif type(create_using) is type(Graph):
             G = create_using()
         else:
@@ -159,8 +157,6 @@ def from_adjlist(offsets, indices, values=None, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -240,8 +236,6 @@ def from_cudf_edgelist(
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -327,8 +321,6 @@ def from_pandas_edgelist(
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -391,8 +383,6 @@ def from_pandas_adjacency(df, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -438,8 +428,6 @@ def from_numpy_array(A, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
@@ -484,8 +472,6 @@ def from_numpy_matrix(A, create_using=Graph):
     elif isinstance(create_using, Graph):
         attrs = {"directed": create_using.is_directed()}
         G = type(create_using)(**attrs)
-    elif isinstance(create_using, DiGraph):
-        G = type(create_using)()
     elif type(create_using) is type(Graph):
         G = create_using()
     else:
diff --git a/python/cugraph/cugraph/structure/graph_classes.py b/python/cugraph/cugraph/structure/graph_classes.py
index 311ee5a5352..add1be1f099 100644
--- a/python/cugraph/cugraph/structure/graph_classes.py
+++ b/python/cugraph/cugraph/structure/graph_classes.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -19,7 +19,6 @@
 )
 import cudf
 import dask_cudf
-import warnings
 
 from cugraph.utilities.utils import import_optional
 
@@ -688,15 +687,6 @@ def add_nodes_from(self, nodes):
     # def properties():
 
 
-class DiGraph(Graph):
-    def __init__(self, m_graph=None):
-        warnings.warn(
-            "DiGraph is deprecated, use Graph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(DiGraph, self).__init__(m_graph, directed=True)
-
-
 class MultiGraph(Graph):
     """
     A Multigraph; a Graph containing more than one edge between vertex pairs.
@@ -714,16 +704,6 @@ def is_multigraph(self):
         return True
 
 
-class MultiDiGraph(MultiGraph):
-    def __init__(self):
-        warnings.warn(
-            "MultiDiGraph is deprecated,\
-                use MultiGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(MultiDiGraph, self).__init__(directed=True)
-
-
 class Tree(Graph):
     """
     A Tree
@@ -920,30 +900,6 @@ def is_bipartite(self):
         return True
 
 
-class BiPartiteDiGraph(BiPartiteGraph):
-    """
-    A Directed Bipartite Graph
-    """
-
-    def __init__(self):
-        warnings.warn(
-            "BiPartiteDiGraph is deprecated,\
- use BiPartiteGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(BiPartiteDiGraph, self).__init__(directed=True)
-
-
-class NPartiteDiGraph(NPartiteGraph):
-    def __init__(self):
-        warnings.warn(
-            "NPartiteDiGraph is deprecated,\
- use NPartiteGraph(directed=True) instead",
-            DeprecationWarning,
-        )
-        super(NPartiteGraph, self).__init__(directed=True)
-
-
 def is_directed(G):
     """
     Returns True if the graph is a directed graph.
diff --git a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
index cef40588879..013ef0c8cfc 100644
--- a/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
+++ b/python/cugraph/cugraph/structure/graph_implementation/simpleDistributedGraph.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -751,15 +751,13 @@ def convert_to_cudf(cp_arrays):
 
         return ddf
 
-    def to_directed(self, DiG):
+    def to_directed(self, G):
         """
         Return a directed representation of the graph.
-        This function sets the type of graph as DiGraph() and returns the
-        directed view.
 
         Returns
         -------
-        G : DiGraph
+        G : Graph(directed=True)
             A directed graph with the same nodes, and each edge (u,v,weights)
             replaced by two directed edges (u,v,weights) and (v,u,weights).
 
diff --git a/python/cugraph/cugraph/testing/utils.py b/python/cugraph/cugraph/testing/utils.py
index 443fdcb9092..ce9acbca9b6 100644
--- a/python/cugraph/cugraph/testing/utils.py
+++ b/python/cugraph/cugraph/testing/utils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -168,16 +168,16 @@ def create_obj_from_csv(
     Return an object based on obj_type populated with the contents of
     csv_file_name
     """
-    if obj_type in [cugraph.Graph, cugraph.DiGraph]:
+    if obj_type in [cugraph.Graph]:
         return generate_cugraph_graph_from_file(
             csv_file_name,
-            directed=(obj_type is cugraph.DiGraph),
+            directed=directed,
             edgevals=edgevals,
         )
     elif isinstance(obj_type, cugraph.Graph):
         return generate_cugraph_graph_from_file(
             csv_file_name,
-            directed=(obj_type.is_directed()),
+            directed=directed,
             edgevals=edgevals,
         )
 
diff --git a/python/cugraph/cugraph/tests/test_balanced_cut.py b/python/cugraph/cugraph/tests/test_balanced_cut.py
index ef21feb35ad..9054d485779 100644
--- a/python/cugraph/cugraph/tests/test_balanced_cut.py
+++ b/python/cugraph/cugraph/tests/test_balanced_cut.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -41,10 +41,10 @@ def random_call(G, partitions):
         for i in range(num_verts):
             assignment.append(random.randint(0, partitions - 1))
 
-        assignment_cu = cudf.DataFrame(assignment, columns=["cluster"])
-        assignment_cu["vertex"] = assignment_cu.index
+        assign_cu = cudf.DataFrame(assignment, columns=["cluster"])
+        assign_cu["vertex"] = assign_cu.index
 
-        score += cugraph.analyzeClustering_edge_cut(G, partitions, assignment_cu)
+        score += cugraph.analyzeClustering_edge_cut(G, partitions, assign_cu)
 
     return set(range(num_verts)), (score / 10.0)
 
@@ -97,28 +97,6 @@ def test_edge_cut_clustering_with_edgevals(graph_file, partitions):
     assert cu_score < rand_score
 
 
-# Test to ensure DiGraph objs are not accepted
-# Test all combinations of default/managed and pooled/non-pooled allocation
-
-
-def test_digraph_rejected():
-    gc.collect()
-
-    df = cudf.DataFrame()
-    df["src"] = cudf.Series(range(10))
-    df["dst"] = cudf.Series(range(10))
-    df["val"] = cudf.Series(range(10))
-
-    with pytest.deprecated_call():
-        G = cugraph.DiGraph()
-    G.from_cudf_edgelist(
-        df, source="src", destination="dst", edge_attr="val", renumber=False
-    )
-
-    with pytest.raises(Exception):
-        cugraph_call(G, 2)
-
-
 @pytest.mark.parametrize("graph_file", DATASETS)
 @pytest.mark.parametrize("partitions", PARTITIONS)
 def test_edge_cut_clustering_with_edgevals_nx(graph_file, partitions):
diff --git a/python/cugraph/cugraph/tests/test_bfs.py b/python/cugraph/cugraph/tests/test_bfs.py
index 679876fa57b..9cda09de87c 100644
--- a/python/cugraph/cugraph/tests/test_bfs.py
+++ b/python/cugraph/cugraph/tests/test_bfs.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -405,7 +405,7 @@ def test_bfs(gpubenchmark, dataset_nxresults_startvertex_spc, cugraph_input_type
         elif cugraph_input_type is nx.Graph:
             cugraph_input_type = nx.DiGraph
 
-    if not isinstance(cugraph_input_type, (cugraph.Graph, cugraph.DiGraph)):
+    if not isinstance(cugraph_input_type, cugraph.Graph):
         G_or_matrix = utils.create_obj_from_csv(dataset, cugraph_input_type)
     else:
         G_or_matrix = G
diff --git a/python/cugraph/cugraph/tests/test_connectivity.py b/python/cugraph/cugraph/tests/test_connectivity.py
index 25cbda02094..9650f5ee559 100644
--- a/python/cugraph/cugraph/tests/test_connectivity.py
+++ b/python/cugraph/cugraph/tests/test_connectivity.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -306,7 +306,7 @@ def test_weak_cc(gpubenchmark, dataset_nxresults_weak, cugraph_input_type):
 
     # cuGraph or nx 'input_type' should have this parameter set to None
     directed = None
-    if not isinstance(cugraph_input_type, (cugraph.Graph, cugraph.DiGraph)):
+    if not isinstance(cugraph_input_type, cugraph.Graph):
         input_G_or_matrix = utils.create_obj_from_csv(
             dataset_path, cugraph_input_type, edgevals=True
         )
@@ -370,7 +370,7 @@ def test_strong_cc(gpubenchmark, dataset_nxresults_strong, cugraph_input_type):
         api_type,
     ) = dataset_nxresults_strong
 
-    if not isinstance(cugraph_input_type, (cugraph.Graph, cugraph.DiGraph)):
+    if not isinstance(cugraph_input_type, cugraph.Graph):
         input_G_or_matrix = utils.create_obj_from_csv(
             dataset_path, cugraph_input_type, edgevals=True
         )
diff --git a/python/cugraph/cugraph/tests/test_convert_matrix.py b/python/cugraph/cugraph/tests/test_convert_matrix.py
index f4c4360aca8..44a2b9d9598 100644
--- a/python/cugraph/cugraph/tests/test_convert_matrix.py
+++ b/python/cugraph/cugraph/tests/test_convert_matrix.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -49,7 +49,7 @@ def test_to_from_pandas(graph_file):
     nx_pdf = nx_pdf[sorted(nx_pdf.columns)]
     nx_pdf.sort_index(inplace=True)
 
-    # create a cugraph DiGraph and convert to pandas adjacency
+    # create a cugraph Directed Graph and convert to pandas adjacency
     cuG = cugraph.from_pandas_edgelist(
         M,
         source="0",
@@ -91,13 +91,17 @@ def test_from_to_numpy(graph_file):
     # Read in the graph
     M = utils.read_csv_for_nx(graph_file, read_weights_in_sp=True)
 
-    # create NetworkX and cugraph DiGraph
+    # create NetworkX and cugraph Directed Graph
     nxG = nx.from_pandas_edgelist(
         M, source="0", target="1", edge_attr="weight", create_using=nx.DiGraph
     )
 
     cuG = cugraph.from_pandas_edgelist(
-        M, source="0", destination="1", edge_attr="weight", create_using=cugraph.DiGraph
+        M,
+        source="0",
+        destination="1",
+        edge_attr="weight",
+        create_using=cugraph.Graph(directed=True),
     )
 
     # convert graphs to numpy array
@@ -112,7 +116,9 @@ def test_from_to_numpy(graph_file):
 
     # Create graphs from numpy array
     new_nxG = nx.from_numpy_array(nparray_nx, create_using=nx.DiGraph)
-    new_cuG = cugraph.from_numpy_array(nparray_cu, create_using=cugraph.DiGraph)
+    new_cuG = cugraph.from_numpy_array(
+        nparray_cu, create_using=cugraph.Graph(directed=True)
+    )
 
     # Assert graphs are same
     exp_pdf = nx.to_pandas_edgelist(new_nxG)
@@ -130,7 +136,9 @@ def test_from_to_numpy(graph_file):
 
     # Create graphs from numpy matrix
     new_nxG = nx.from_numpy_matrix(npmatrix_nx, create_using=nx.DiGraph)
-    new_cuG = cugraph.from_numpy_matrix(npmatrix_cu, create_using=cugraph.DiGraph)
+    new_cuG = cugraph.from_numpy_matrix(
+        npmatrix_cu, create_using=cugraph.Graph(directed=True)
+    )
 
     # Assert graphs are same
     exp_pdf = nx.to_pandas_edgelist(new_nxG)
@@ -185,10 +193,10 @@ def test_from_adjlist(graph_file):
         G1 = cugraph.from_adjlist(cu_offsets, cu_indices, cu_vals, create_using=33)
 
     G1 = cugraph.from_adjlist(
-        cu_offsets, cu_indices, cu_vals, create_using=cugraph.DiGraph
+        cu_offsets, cu_indices, cu_vals, create_using=cugraph.Graph(directed=True)
     )
     G2 = cugraph.from_adjlist(
-        pd_offsets, pd_indices, pd_vals, create_using=cugraph.DiGraph
+        pd_offsets, pd_indices, pd_vals, create_using=cugraph.Graph(directed=True)
     )
 
     assert G1.AdjList == G2.AdjList
diff --git a/python/cugraph/cugraph/tests/test_graph.py b/python/cugraph/cugraph/tests/test_graph.py
index 4667b8c9976..c795453d8a0 100644
--- a/python/cugraph/cugraph/tests/test_graph.py
+++ b/python/cugraph/cugraph/tests/test_graph.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -397,7 +397,10 @@ def test_consolidation(graph_file):
         df, source="source", target="target", create_using=nx.DiGraph
     )
     G = cugraph.from_cudf_edgelist(
-        ddf, source="source", destination="target", create_using=cugraph.DiGraph
+        ddf,
+        source="source",
+        destination="target",
+        create_using=cugraph.Graph(directed=True),
     )
 
     t1 = time.time()
@@ -683,7 +686,7 @@ def test_graph_init_with_multigraph():
     cMG.from_cudf_edgelist(gdf, source="src", destination="dst")
     cugraph.Graph(m_graph=cMG)
 
-    cDiMG = cugraph.MultiDiGraph()  # deprecated, but should still work
+    cDiMG = cugraph.MultiGraph(directed=True)
     cDiMG.from_cudf_edgelist(gdf, source="src", destination="dst")
     cugraph.Graph(m_graph=cDiMG)
 
diff --git a/python/cugraph/cugraph/tests/test_louvain.py b/python/cugraph/cugraph/tests/test_louvain.py
index cbd243e888d..8717e1bfc97 100644
--- a/python/cugraph/cugraph/tests/test_louvain.py
+++ b/python/cugraph/cugraph/tests/test_louvain.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -17,7 +17,7 @@
 import pytest
 
 import cugraph
-import cupy
+import cupyx
 import cudf
 from cugraph.testing import utils
 from cugraph.experimental.datasets import DATASETS_UNDIRECTED, karate_asymmetric
@@ -116,7 +116,7 @@ def test_louvain_csr_graph(is_weighted):
     karate = DATASETS_UNDIRECTED[0]
     df = karate.get_edgelist()
 
-    M = cupy.sparse.coo_matrix(
+    M = cupyx.scipy.sparse.coo_matrix(
         (df["wgt"].to_cupy(), (df["src"].to_cupy(), df["dst"].to_cupy()))
     )
     M = M.tocsr()
diff --git a/python/cugraph/cugraph/tests/test_multigraph.py b/python/cugraph/cugraph/tests/test_multigraph.py
index c67454181c1..e6aad439fbd 100644
--- a/python/cugraph/cugraph/tests/test_multigraph.py
+++ b/python/cugraph/cugraph/tests/test_multigraph.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -84,7 +84,7 @@ def test_Graph_from_MultiGraph(graph_file):
         edge_attr="weight",
         create_using=nx.MultiGraph(),
     )
-    Gd = cugraph.DiGraph(GdM)
+    Gd = cugraph.Graph(GdM, directed=True)
     Gnxd = nx.DiGraph(GnxdM)
     assert Gnxd.number_of_edges() == Gd.number_of_edges()
 
diff --git a/python/cugraph/cugraph/tests/test_nx_convert.py b/python/cugraph/cugraph/tests/test_nx_convert.py
index e3340534f10..3d4ef8e6d3e 100644
--- a/python/cugraph/cugraph/tests/test_nx_convert.py
+++ b/python/cugraph/cugraph/tests/test_nx_convert.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -81,7 +81,7 @@ def test_networkx_compatibility(graph_file):
         M, source="0", target="1", edge_attr="weight", create_using=nx.DiGraph()
     )
 
-    # create a cuGraph DiGraph
+    # create a cuGraph Directed Graph
     gdf = cudf.from_pandas(M)
     gdf = gdf.rename(columns={"weight": "weights"})
     cuG = cugraph.from_cudf_edgelist(
@@ -89,7 +89,7 @@ def test_networkx_compatibility(graph_file):
         source="0",
         destination="1",
         edge_attr="weights",
-        create_using=cugraph.DiGraph,
+        create_using=cugraph.Graph(directed=True),
     )
 
     _compare_graphs(nxG, cuG)
diff --git a/python/cugraph/cugraph/tests/test_random_walks.py b/python/cugraph/cugraph/tests/test_random_walks.py
index c951b6a2bc7..00080d312d1 100644
--- a/python/cugraph/cugraph/tests/test_random_walks.py
+++ b/python/cugraph/cugraph/tests/test_random_walks.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.:
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.:
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -43,7 +43,7 @@ def calc_random_walks(graph_file, directed=False, max_depth=None, use_padding=Fa
     parameters
     ----------
     G : cuGraph.Graph or networkx.Graph
-        The graph can be either directed (DiGraph) or undirected (Graph).
+        The graph can be either directed or undirected.
         Weights in the graph are ignored.
         Use weight parameter if weights need to be considered
         (currently not supported)
@@ -174,7 +174,7 @@ def test_random_walks(
     df_G['dst_0'] = df_G['dst'] + 1000
 
     if directed:
-        G = cugraph.DiGraph()
+        G = cugraph.Graph(directed=True)
     else:
         G = cugraph.Graph()
     G.from_cudf_edgelist(df_G, source=['src', 'src_0'],
diff --git a/python/cugraph/cugraph/tests/test_sssp.py b/python/cugraph/cugraph/tests/test_sssp.py
index 1e13116daea..65f248d16d9 100644
--- a/python/cugraph/cugraph/tests/test_sssp.py
+++ b/python/cugraph/cugraph/tests/test_sssp.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -223,7 +223,7 @@ def test_sssp(gpubenchmark, dataset_source_nxresults, cugraph_input_type):
     # Extract the params generated from the fixture
     (G, dataset_path, source, nx_paths, Gnx) = dataset_source_nxresults
 
-    if not isinstance(cugraph_input_type, (cugraph.Graph, cugraph.DiGraph)):
+    if not isinstance(cugraph_input_type, cugraph.Graph):
         input_G_or_matrix = utils.create_obj_from_csv(
             dataset_path, cugraph_input_type, edgevals=True
         )
diff --git a/python/cugraph/cugraph/tests/test_subgraph_extraction.py b/python/cugraph/cugraph/tests/test_subgraph_extraction.py
index fcfd063b61a..bf3137974ef 100644
--- a/python/cugraph/cugraph/tests/test_subgraph_extraction.py
+++ b/python/cugraph/cugraph/tests/test_subgraph_extraction.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -40,13 +40,8 @@ def compare_edges(cg, nxg):
 
 
 def cugraph_call(M, verts, directed=True):
-    # directed is used to create either a Graph or DiGraph so the returned
     # cugraph can be compared to nx graph of same type.
-    if directed:
-        G = cugraph.DiGraph()
-    else:
-        G = cugraph.Graph()
-    cu_M = cudf.DataFrame()
+    G = cugraph.Graph(directed=directed)
 
     cu_M = cudf.from_pandas(M)
 
@@ -75,8 +70,8 @@ def test_subgraph_extraction_DiGraph(graph_file):
     verts[0] = 0
     verts[1] = 1
     verts[2] = 17
-    cu_sg = cugraph_call(M, verts)
-    nx_sg = nx_call(M, verts)
+    cu_sg = cugraph_call(M, verts, True)
+    nx_sg = nx_call(M, verts, True)
     assert compare_edges(cu_sg, nx_sg)
 
 
diff --git a/python/cugraph/cugraph/traversal/bfs.py b/python/cugraph/cugraph/traversal/bfs.py
index a5eb463efb8..96e8eb02a77 100644
--- a/python/cugraph/cugraph/traversal/bfs.py
+++ b/python/cugraph/cugraph/traversal/bfs.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -17,7 +17,7 @@
 from pylibcugraph import ResourceHandle
 from pylibcugraph import bfs as pylibcugraph_bfs
 
-from cugraph.structure.graph_classes import Graph, DiGraph
+from cugraph.structure.graph_classes import Graph
 from cugraph.utilities import (
     ensure_cugraph_obj,
     is_matrix_type,
@@ -43,7 +43,7 @@ def _ensure_args(G, start, i_start, directed):
 
     G_type = type(G)
     # Check for Graph-type inputs
-    if (G_type in [Graph, DiGraph]) or is_nx_graph_type(G_type):
+    if G_type is Graph or is_nx_graph_type(G_type):
         if directed is not None:
             raise TypeError("'directed' cannot be specified for a " "Graph-type input")
 
@@ -93,7 +93,7 @@ def _convert_df_to_output_type(df, input_type):
     Given a cudf.DataFrame df, convert it to a new type appropriate for the
     graph algos in this module, based on input_type.
     """
-    if input_type in [Graph, DiGraph]:
+    if input_type is Graph:
         return df
 
     elif is_nx_graph_type(input_type):
@@ -105,8 +105,8 @@ def _convert_df_to_output_type(df, input_type):
         #   predecessor: cupy.ndarray
         sorted_df = df.sort_values("vertex")
         if is_cp_matrix_type(input_type):
-            distances = cp.fromDlpack(sorted_df["distance"].to_dlpack())
-            preds = cp.fromDlpack(sorted_df["predecessor"].to_dlpack())
+            distances = cp.from_dlpack(sorted_df["distance"].to_dlpack())
+            preds = cp.from_dlpack(sorted_df["predecessor"].to_dlpack())
             return (distances, preds)
         else:
             distances = sorted_df["distance"].to_numpy()
diff --git a/python/cugraph/cugraph/traversal/ms_bfs.py b/python/cugraph/cugraph/traversal/ms_bfs.py
index cce4546c8b5..df624e453ee 100644
--- a/python/cugraph/cugraph/traversal/ms_bfs.py
+++ b/python/cugraph/cugraph/traversal/ms_bfs.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2021-2022, NVIDIA CORPORATION.
+# Copyright (c) 2021-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -24,7 +24,7 @@ def _get_feasibility(G, sources, components=None, depth_limit=None):
 
     Parameters
     ----------
-    G : cugraph.Graph or cugraph.DiGraph
+    G : cugraph.Graph
         The adjacency list will be computed if not already present.
 
     sources :  cudf.Series
@@ -112,7 +112,7 @@ def concurrent_bfs(Graphs, sources, depth_limit=None, offload=False):
 
     Parameters
     ----------
-    Graphs : list of cugraph.Graph or cugraph.DiGraph
+    Graphs : list of cugraph.Graph
         The adjacency lists will be computed if not already present.
 
     sources : list of cudf.Series
@@ -152,7 +152,7 @@ def concurrent_bfs(Graphs, sources, depth_limit=None, offload=False):
              to help us prioritize"
     )
     if not isinstance(Graphs, list):
-        raise TypeError("Graphs should be a list of cugraph.Graph or cugraph.DiGraph")
+        raise TypeError("Graphs should be a list of cugraph.Graph")
     if not isinstance(sources, list):
         raise TypeError("sources should be a list of cudf.Series")
     if len(Graphs) != len(sources):
@@ -186,7 +186,7 @@ def multi_source_bfs(G, sources, components=None, depth_limit=None, offload=Fals
 
     Parameters
     ----------
-    G : cugraph.Graph or cugraph.DiGraph
+    G : cugraph.Graph
         The adjacency list will be computed if not already present.
 
     sources :  cudf.Series
diff --git a/python/cugraph/cugraph/traversal/sssp.py b/python/cugraph/cugraph/traversal/sssp.py
index 7e04ff7678b..020d5933365 100644
--- a/python/cugraph/cugraph/traversal/sssp.py
+++ b/python/cugraph/cugraph/traversal/sssp.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2019-2022, NVIDIA CORPORATION.
+# Copyright (c) 2019-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -15,7 +15,7 @@
 import warnings
 
 import cudf
-from cugraph.structure import Graph, DiGraph, MultiGraph, MultiDiGraph
+from cugraph.structure import Graph, MultiGraph
 from cugraph.utilities import (
     ensure_cugraph_obj,
     is_matrix_type,
@@ -46,7 +46,7 @@ def _ensure_args(
 
     G_type = type(G)
     # Check for Graph-type inputs
-    if (G_type in [Graph, DiGraph]) or is_nx_graph_type(G_type):
+    if G_type is Graph or is_nx_graph_type(G_type):
         # FIXME: Improve Graph-type checking
         exc_value = "'%s' cannot be specified for a Graph-type input"
         if directed is not None:
@@ -95,7 +95,7 @@ def _convert_df_to_output_type(df, input_type, return_predecessors):
     return_predecessors is only used for return values from cupy/scipy input
     types.
     """
-    if input_type in [Graph, DiGraph, MultiGraph, MultiDiGraph]:
+    if input_type in [Graph, MultiGraph]:
         return df
 
     elif is_nx_graph_type(input_type):
diff --git a/python/cugraph/cugraph/utilities/utils.py b/python/cugraph/cugraph/utilities/utils.py
index 1cb15a67851..8cbdf6a8c21 100644
--- a/python/cugraph/cugraph/utilities/utils.py
+++ b/python/cugraph/cugraph/utilities/utils.py
@@ -1,4 +1,4 @@
-# Copyright (c) 2020-2022, NVIDIA CORPORATION.
+# Copyright (c) 2020-2023, NVIDIA CORPORATION.
 # Licensed under the Apache License, Version 2.0 (the "License");
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
@@ -257,7 +257,7 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
 
     """
     Convert the input obj - if possible - to a cuGraph Graph-type obj (Graph,
-    DiGraph, etc.) and return a tuple of (cugraph Graph-type obj, original
+    etc.) and return a tuple of (cugraph Graph-type obj, original
     input obj type). If matrix_graph_type is specified, it is used as the
     cugraph Graph-type obj to create when converting from a matrix type.
     """
@@ -363,10 +363,9 @@ def is_nx_graph_type(g):
 
 def is_cugraph_graph_type(g):
     # FIXME: importing here to avoid circular import
-    from cugraph.structure import Graph, DiGraph, MultiGraph, MultiDiGraph
+    from cugraph.structure import Graph, MultiGraph
 
-    # FIXME: Remove DiGraph when support is dropped
-    return g in [Graph, DiGraph, MultiGraph, MultiDiGraph]
+    return g in [Graph, MultiGraph]
 
 
 def renumber_vertex_pair(input_graph, vertex_pair):