From 3646180c78293550e99dff05414f8daac3fea6c0 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <rratzel@nvidia.com>
Date: Mon, 27 Sep 2021 20:17:05 -0500
Subject: [PATCH 1/4] Fixed example formatting for RMAT docstring, added
 utility to check for Nx types without requiring Nx.

---
 .../centrality/betweenness_centrality.py      |   5 +-
 .../cugraph/centrality/katz_centrality.py     |   7 +-
 python/cugraph/cugraph/community/ecg.py       |   7 +-
 python/cugraph/cugraph/community/egonet.py    |   9 +-
 .../cugraph/community/ktruss_subgraph.py      |   7 +-
 python/cugraph/cugraph/community/leiden.py    |   7 +-
 python/cugraph/cugraph/community/louvain.py   |   7 +-
 .../cugraph/community/spectral_clustering.py  |  13 +-
 .../cugraph/community/subgraph_extraction.py  |   8 +-
 .../cugraph/community/triangle_count.py       |   4 +-
 .../cugraph/components/connectivity.py        |  27 +--
 python/cugraph/cugraph/cores/core_number.py   |   7 +-
 python/cugraph/cugraph/cores/k_core.py        |   7 +-
 python/cugraph/cugraph/generators/rmat.py     |  30 ++--
 python/cugraph/cugraph/layout/force_atlas2.py |   4 +-
 python/cugraph/cugraph/link_analysis/hits.py  |   7 +-
 .../cugraph/cugraph/link_analysis/pagerank.py |  11 +-
 .../cugraph/link_prediction/jaccard.py        |   9 +-
 .../cugraph/link_prediction/overlap.py        |   9 +-
 .../cugraph/link_prediction/sorensen.py       |   9 +-
 .../cugraph/cugraph/sampling/random_walks.py  |   4 +-
 .../cugraph/structure/graph_classes.py        |  10 +-
 python/cugraph/cugraph/traversal/bfs.py       |  27 +--
 python/cugraph/cugraph/traversal/sssp.py      |  27 +--
 .../cugraph/tree/minimum_spanning_tree.py     |   9 +-
 .../tree/minimum_spanning_tree_wrapper.pyx    |   4 +-
 python/cugraph/cugraph/utilities/__init__.py  |   7 +-
 .../cugraph/cugraph/utilities/nx_factory.py   |  50 +-----
 python/cugraph/cugraph/utilities/utils.py     | 156 +++++++++++++-----
 29 files changed, 244 insertions(+), 244 deletions(-)

diff --git a/python/cugraph/cugraph/centrality/betweenness_centrality.py b/python/cugraph/cugraph/centrality/betweenness_centrality.py
index 3b7cfe6b68f..1dc79e39eb5 100644
--- a/python/cugraph/cugraph/centrality/betweenness_centrality.py
+++ b/python/cugraph/cugraph/centrality/betweenness_centrality.py
@@ -18,6 +18,7 @@
 from cugraph.centrality import edge_betweenness_centrality_wrapper
 from cugraph.utilities import df_edge_score_to_dictionary
 from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import ensure_cugraph_obj_for_nx
 import cugraph
 
 
@@ -127,7 +128,7 @@ def betweenness_centrality(
     if result_dtype not in [np.float32, np.float64]:
         raise TypeError("result type can only be np.float32 or np.float64")
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     vertices = _initialize_vertices(G, k, seed)
 
@@ -249,7 +250,7 @@ def edge_betweenness_centrality(
     if result_dtype not in [np.float32, np.float64]:
         raise TypeError("result type can only be np.float32 or np.float64")
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
     vertices = _initialize_vertices(G, k, seed)
 
     df = edge_betweenness_centrality_wrapper.edge_betweenness_centrality(
diff --git a/python/cugraph/cugraph/centrality/katz_centrality.py b/python/cugraph/cugraph/centrality/katz_centrality.py
index 19432429f1e..af6c73dc366 100644
--- a/python/cugraph/cugraph/centrality/katz_centrality.py
+++ b/python/cugraph/cugraph/centrality/katz_centrality.py
@@ -12,6 +12,9 @@
 # limitations under the License.
 
 from cugraph.centrality import katz_centrality_wrapper
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 import cugraph
 
 
@@ -112,7 +115,7 @@ def katz_centrality(
                 "currently not supported"
         )
 
-    G, isNx = cugraph.utilities.check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if nstart is not None:
         if G.renumbered is True:
@@ -130,7 +133,7 @@ def katz_centrality(
         df = G.unrenumber(df, "vertex")
 
     if isNx is True:
-        dict = cugraph.utilities.df_score_to_dictionary(df, 'katz_centrality')
+        dict = df_score_to_dictionary(df, 'katz_centrality')
         return dict
     else:
         return df
diff --git a/python/cugraph/cugraph/community/ecg.py b/python/cugraph/cugraph/community/ecg.py
index eb27f5d7ae0..17deacecd6c 100644
--- a/python/cugraph/cugraph/community/ecg.py
+++ b/python/cugraph/cugraph/community/ecg.py
@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.community import ecg_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
@@ -70,7 +71,7 @@ def ecg(input_graph, min_weight=0.05, ensemble_size=16, weight=None):
 
     """
 
-    input_graph, isNx = check_nx_graph(input_graph, weight)
+    input_graph, isNx = ensure_cugraph_obj_for_nx(input_graph, weight)
 
     parts = ecg_wrapper.ecg(input_graph, min_weight, ensemble_size)
 
diff --git a/python/cugraph/cugraph/community/egonet.py b/python/cugraph/cugraph/community/egonet.py
index 5ae025f1203..2fcafa5795b 100644
--- a/python/cugraph/cugraph/community/egonet.py
+++ b/python/cugraph/cugraph/community/egonet.py
@@ -15,20 +15,17 @@
 import cudf
 from cugraph.utilities import (
     ensure_cugraph_obj,
-    import_optional,
+    is_nx_graph_type,
 )
 from cugraph.utilities import cugraph_to_nx
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
 
 def _convert_graph_to_output_type(G, input_type):
     """
     Given a cugraph.Graph, convert it to a new type appropriate for the
     graph algos in this module, based on input_type.
     """
-    if (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    if is_nx_graph_type(G):
         return cugraph_to_nx(G)
 
     else:
@@ -40,7 +37,7 @@ def _convert_df_series_to_output_type(df, offsets, 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 (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    if is_nx_graph_type(input_type):
         return df.to_pandas(), offsets.values_host.tolist()
 
     else:
diff --git a/python/cugraph/cugraph/community/ktruss_subgraph.py b/python/cugraph/cugraph/community/ktruss_subgraph.py
index afa7d66d31d..599108ca4cf 100644
--- a/python/cugraph/cugraph/community/ktruss_subgraph.py
+++ b/python/cugraph/cugraph/community/ktruss_subgraph.py
@@ -13,8 +13,9 @@
 
 from cugraph.community import ktruss_subgraph_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import cugraph_to_nx
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
 
 from numba import cuda
 
@@ -67,7 +68,7 @@ def k_truss(G, k):
 
     _ensure_compatible_cuda_version()
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True:
         k_sub = ktruss_subgraph(G, k)
diff --git a/python/cugraph/cugraph/community/leiden.py b/python/cugraph/cugraph/community/leiden.py
index 641cf552192..b2f868a45c0 100644
--- a/python/cugraph/cugraph/community/leiden.py
+++ b/python/cugraph/cugraph/community/leiden.py
@@ -13,8 +13,9 @@
 
 from cugraph.community import leiden_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def leiden(G, max_iter=100, resolution=1.):
@@ -72,7 +73,7 @@ def leiden(G, max_iter=100, resolution=1.):
     >>> G.from_cudf_edgelist(M, source='0', destination='1')
     >>> parts, modularity_score = cugraph.leiden(G)
     """
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception(f"input graph must be undirected was {type(G)}")
diff --git a/python/cugraph/cugraph/community/louvain.py b/python/cugraph/cugraph/community/louvain.py
index a761e060038..2261cc8ae85 100644
--- a/python/cugraph/cugraph/community/louvain.py
+++ b/python/cugraph/cugraph/community/louvain.py
@@ -13,8 +13,9 @@
 
 from cugraph.community import louvain_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def louvain(G, max_iter=100, resolution=1.):
@@ -73,7 +74,7 @@ def louvain(G, max_iter=100, resolution=1.):
     >>> parts, modularity_score = cugraph.louvain(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception("input graph must be undirected")
diff --git a/python/cugraph/cugraph/community/spectral_clustering.py b/python/cugraph/cugraph/community/spectral_clustering.py
index 06294af00c9..0e9579b6b44 100644
--- a/python/cugraph/cugraph/community/spectral_clustering.py
+++ b/python/cugraph/cugraph/community/spectral_clustering.py
@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.community import spectral_clustering_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def spectralBalancedCutClustering(
@@ -75,7 +76,7 @@ def spectralBalancedCutClustering(
 
     # Error checking in C++ code
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = spectral_clustering_wrapper.spectralBalancedCutClustering(
         G,
@@ -152,7 +153,7 @@ def spectralModularityMaximizationClustering(
 
     # Error checking in C++ code
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = spectral_clustering_wrapper.spectralModularityMaximizationClustering(
         G,
@@ -222,7 +223,7 @@ def analyzeClustering_modularity(G, n_clusters, clustering,
     if type(cluster_col_name) is not str:
         raise Exception("cluster_col_name must be a string")
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         clustering = G.add_internal_vertex_id(clustering,
@@ -288,7 +289,7 @@ def analyzeClustering_edge_cut(G, n_clusters, clustering,
     if type(cluster_col_name) is not str:
         raise Exception("cluster_col_name must be a string")
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         clustering = G.add_internal_vertex_id(clustering,
diff --git a/python/cugraph/cugraph/community/subgraph_extraction.py b/python/cugraph/cugraph/community/subgraph_extraction.py
index 2df6e037d71..14173311a94 100644
--- a/python/cugraph/cugraph/community/subgraph_extraction.py
+++ b/python/cugraph/cugraph/community/subgraph_extraction.py
@@ -12,9 +12,11 @@
 # limitations under the License.
 
 from cugraph.community import subgraph_extraction_wrapper
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
+
 import cudf
-from cugraph.utilities import cugraph_to_nx
 
 
 def subgraph(G, vertices):
@@ -53,7 +55,7 @@ def subgraph(G, vertices):
     >>> Sg = cugraph.subgraph(G, sverts)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if G.renumbered:
         if isinstance(vertices, cudf.DataFrame):
diff --git a/python/cugraph/cugraph/community/triangle_count.py b/python/cugraph/cugraph/community/triangle_count.py
index d28424a513e..fce339a1b7c 100644
--- a/python/cugraph/cugraph/community/triangle_count.py
+++ b/python/cugraph/cugraph/community/triangle_count.py
@@ -13,7 +13,7 @@
 
 from cugraph.community import triangle_count_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import ensure_cugraph_obj_for_nx
 
 
 def triangles(G):
@@ -47,7 +47,7 @@ def triangles(G):
     >>> count = cugraph.triangles(G)
     """
 
-    G, _ = check_nx_graph(G)
+    G, _ = ensure_cugraph_obj_for_nx(G)
 
     if type(G) is not Graph:
         raise Exception("input graph must be undirected")
diff --git a/python/cugraph/cugraph/components/connectivity.py b/python/cugraph/cugraph/components/connectivity.py
index 94eea312fb9..c1fdc6ac135 100644
--- a/python/cugraph/cugraph/components/connectivity.py
+++ b/python/cugraph/cugraph/components/connectivity.py
@@ -16,30 +16,12 @@
                                ensure_cugraph_obj,
                                is_matrix_type,
                                is_cp_matrix_type,
-                               import_optional,
+                               is_nx_graph_type,
+                               cupy_package as cp,
                                )
 from cugraph.structure import Graph, DiGraph
 from cugraph.components import connectivity_wrapper
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
-cp = import_optional("cupy")
-cp_coo_matrix = import_optional("coo_matrix",
-                                import_from="cupyx.scipy.sparse.coo")
-cp_csr_matrix = import_optional("csr_matrix",
-                                import_from="cupyx.scipy.sparse.csr")
-cp_csc_matrix = import_optional("csc_matrix",
-                                import_from="cupyx.scipy.sparse.csc")
-
-sp = import_optional("scipy")
-sp_coo_matrix = import_optional("coo_matrix",
-                                import_from="scipy.sparse.coo")
-sp_csr_matrix = import_optional("csr_matrix",
-                                import_from="scipy.sparse.csr")
-sp_csc_matrix = import_optional("csc_matrix",
-                                import_from="scipy.sparse.csc")
-
 
 def _ensure_args(api_name, G, directed, connection, return_labels):
     """
@@ -49,8 +31,7 @@ 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 \
-       ((nx is not None) and (G_type in [nx.Graph, nx.DiGraph])):
+    if (G_type in [Graph, DiGraph]) 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")
@@ -92,7 +73,7 @@ def _convert_df_to_output_type(df, input_type, return_labels):
     if input_type in [Graph, DiGraph]:
         return df
 
-    elif (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    elif is_nx_graph_type(input_type):
         return df_score_to_dictionary(df, "labels", "vertex")
 
     elif is_matrix_type(input_type):
diff --git a/python/cugraph/cugraph/cores/core_number.py b/python/cugraph/cugraph/cores/core_number.py
index f1f6de3f0de..0d0fc0ea369 100644
--- a/python/cugraph/cugraph/cores/core_number.py
+++ b/python/cugraph/cugraph/cores/core_number.py
@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.cores import core_number_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def core_number(G):
@@ -52,7 +53,7 @@ def core_number(G):
     >>> cn = cugraph.core_number(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = core_number_wrapper.core_number(G)
 
diff --git a/python/cugraph/cugraph/cores/k_core.py b/python/cugraph/cugraph/cores/k_core.py
index 17a3baf9c4c..be720cab30c 100644
--- a/python/cugraph/cugraph/cores/k_core.py
+++ b/python/cugraph/cugraph/cores/k_core.py
@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.cores import k_core_wrapper, core_number_wrapper
-from cugraph.utilities import cugraph_to_nx
-from cugraph.utilities import check_nx_graph
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
 from cugraph.structure.graph_classes import Graph
 
 
@@ -59,7 +60,7 @@ def k_core(G, k=None, core_number=None):
     >>> KCoreGraph = cugraph.k_core(G)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     mytype = type(G)
     KCoreGraph = mytype()
diff --git a/python/cugraph/cugraph/generators/rmat.py b/python/cugraph/cugraph/generators/rmat.py
index 8077a6fbacd..859f99b87c4 100644
--- a/python/cugraph/cugraph/generators/rmat.py
+++ b/python/cugraph/cugraph/generators/rmat.py
@@ -290,21 +290,21 @@ def rmat(
 
     Examples
     --------
-    import cugraph
-    from cugraph.generators import rmat
-
-    df = rmat(
-        scale,
-        (2**scale)*edgefactor,
-        0.1,
-        0.2,
-        0.3,
-        seed or 42,
-        clip_and_flip=False,
-        scramble_vertex_ids=True,
-        create_using=None,  # return edgelist instead of Graph instance
-        mg=False
-        )
+    >>> import cugraph
+    >>> from cugraph.generators import rmat
+    >>>
+    >>> df = rmat(
+    ...    scale,
+    ...    (2**scale)*edgefactor,
+    ...    0.1,
+    ...    0.2,
+    ...    0.3,
+    ...    seed or 42,
+    ...    clip_and_flip=False,
+    ...    scramble_vertex_ids=True,
+    ...    create_using=None,  # return edgelist instead of Graph instance
+    ...    mg=False
+    ... )
     """
 
     _ensure_args_rmat(scale, num_edges, a, b, c, seed, clip_and_flip,
diff --git a/python/cugraph/cugraph/layout/force_atlas2.py b/python/cugraph/cugraph/layout/force_atlas2.py
index 00d920e1921..623879735d1 100644
--- a/python/cugraph/cugraph/layout/force_atlas2.py
+++ b/python/cugraph/cugraph/layout/force_atlas2.py
@@ -12,7 +12,7 @@
 # limitations under the License.
 
 from cugraph.layout import force_atlas2_wrapper
-import cugraph
+from cugraph.utilities import ensure_cugraph_obj_for_nx
 
 
 def force_atlas2(
@@ -107,7 +107,7 @@ def on_train_end(self, positions):
             GPU data frame of size V containing three columns:
             the vertex identifiers and the x and y positions.
     """
-    input_graph, isNx = cugraph.utilities.check_nx_graph(input_graph)
+    input_graph, isNx = ensure_cugraph_obj_for_nx(input_graph)
 
     if pos_list is not None:
         if input_graph.renumbered is True:
diff --git a/python/cugraph/cugraph/link_analysis/hits.py b/python/cugraph/cugraph/link_analysis/hits.py
index f7510a6a0e9..7176d1c66a1 100644
--- a/python/cugraph/cugraph/link_analysis/hits.py
+++ b/python/cugraph/cugraph/link_analysis/hits.py
@@ -12,8 +12,9 @@
 # limitations under the License.
 
 from cugraph.link_analysis import hits_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_score_to_dictionary
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                               )
 
 
 def hits(G, max_iter=100, tol=1.0e-5, nstart=None, normalized=True):
@@ -74,7 +75,7 @@ def hits(G, max_iter=100, tol=1.0e-5, nstart=None, normalized=True):
     >>> hits = cugraph.hits(G, max_iter = 50)
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     df = hits_wrapper.hits(G, max_iter, tol)
 
diff --git a/python/cugraph/cugraph/link_analysis/pagerank.py b/python/cugraph/cugraph/link_analysis/pagerank.py
index b875246fb03..6bb2fcff214 100644
--- a/python/cugraph/cugraph/link_analysis/pagerank.py
+++ b/python/cugraph/cugraph/link_analysis/pagerank.py
@@ -11,10 +11,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from cugraph.link_analysis import pagerank_wrapper
-import cugraph
 import cudf
 
+from cugraph.link_analysis import pagerank_wrapper
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_score_to_dictionary,
+                              )
+
 
 def pagerank(
     G, alpha=0.85, personalization=None, max_iter=100, tol=1.0e-5, nstart=None,
@@ -95,7 +98,7 @@ def pagerank(
     >>> pr = cugraph.pagerank(G, alpha = 0.85, max_iter = 500, tol = 1.0e-05)
     """
 
-    G, isNx = cugraph.utilities.check_nx_graph(G, weight)
+    G, isNx = ensure_cugraph_obj_for_nx(G, weight)
 
     if personalization is not None:
         if not isinstance(personalization, cudf.DataFrame):
@@ -130,6 +133,6 @@ def pagerank(
         df = G.unrenumber(df, "vertex")
 
     if isNx is True:
-        return cugraph.utilities.df_score_to_dictionary(df, 'pagerank')
+        return df_score_to_dictionary(df, 'pagerank')
     else:
         return df
diff --git a/python/cugraph/cugraph/link_prediction/jaccard.py b/python/cugraph/cugraph/link_prediction/jaccard.py
index 689f5d2e768..e1e4b9c875e 100644
--- a/python/cugraph/cugraph/link_prediction/jaccard.py
+++ b/python/cugraph/cugraph/link_prediction/jaccard.py
@@ -14,9 +14,10 @@
 import cudf
 from cugraph.structure.graph_classes import Graph
 from cugraph.link_prediction import jaccard_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_edge_score_to_dictionary
-from cugraph.utilities import renumber_vertex_pair
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_edge_score_to_dictionary,
+                               renumber_vertex_pair,
+                               )
 
 
 def jaccard(input_graph, vertex_pair=None):
@@ -168,7 +169,7 @@ def jaccard_coefficient(G, ebunch=None):
     """
     vertex_pair = None
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True and ebunch is not None:
         vertex_pair = cudf.DataFrame(ebunch)
diff --git a/python/cugraph/cugraph/link_prediction/overlap.py b/python/cugraph/cugraph/link_prediction/overlap.py
index 52f39a7733c..f37a9f94d92 100644
--- a/python/cugraph/cugraph/link_prediction/overlap.py
+++ b/python/cugraph/cugraph/link_prediction/overlap.py
@@ -13,9 +13,10 @@
 
 from cugraph.link_prediction import overlap_wrapper
 import cudf
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_edge_score_to_dictionary
-from cugraph.utilities import renumber_vertex_pair
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_edge_score_to_dictionary,
+                               renumber_vertex_pair,
+                               )
 
 
 def overlap_coefficient(G, ebunch=None):
@@ -25,7 +26,7 @@ def overlap_coefficient(G, ebunch=None):
     """
     vertex_pair = None
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True and ebunch is not None:
         vertex_pair = cudf.DataFrame(ebunch)
diff --git a/python/cugraph/cugraph/link_prediction/sorensen.py b/python/cugraph/cugraph/link_prediction/sorensen.py
index 2499f11d6b6..376eaaeef1b 100644
--- a/python/cugraph/cugraph/link_prediction/sorensen.py
+++ b/python/cugraph/cugraph/link_prediction/sorensen.py
@@ -14,9 +14,10 @@
 import cudf
 from cugraph.structure.graph_classes import Graph
 from cugraph.link_prediction import jaccard_wrapper
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import df_edge_score_to_dictionary
-from cugraph.utilities import renumber_vertex_pair
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               df_edge_score_to_dictionary,
+                               renumber_vertex_pair,
+                               )
 
 
 def sorensen(input_graph, vertex_pair=None):
@@ -136,7 +137,7 @@ def sorensen_coefficient(G, ebunch=None):
     """
     vertex_pair = None
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True and ebunch is not None:
         vertex_pair = cudf.DataFrame(ebunch)
diff --git a/python/cugraph/cugraph/sampling/random_walks.py b/python/cugraph/cugraph/sampling/random_walks.py
index fc21abd3bc4..69ea6733f6a 100644
--- a/python/cugraph/cugraph/sampling/random_walks.py
+++ b/python/cugraph/cugraph/sampling/random_walks.py
@@ -13,7 +13,7 @@
 
 import cudf
 from cugraph.sampling import random_walks_wrapper
-import cugraph
+from cugraph.utilities import ensure_cugraph_obj_for_nx
 
 
 def random_walks(G,
@@ -57,7 +57,7 @@ def random_walks(G,
     if max_depth is None:
         raise TypeError("must specify a 'max_depth'")
 
-    G, _ = cugraph.utilities.check_nx_graph(G)
+    G, _ = ensure_cugraph_obj_for_nx(G)
 
     if start_vertices is int:
         start_vertices = [start_vertices]
diff --git a/python/cugraph/cugraph/structure/graph_classes.py b/python/cugraph/cugraph/structure/graph_classes.py
index f8a944442fb..744e19966ab 100644
--- a/python/cugraph/cugraph/structure/graph_classes.py
+++ b/python/cugraph/cugraph/structure/graph_classes.py
@@ -298,12 +298,8 @@ def from_pandas_edgelist(
         >>> G.from_pandas_edgelist(df, source='0', destination='1',
                                  edge_attr='2', renumber=False)
         """
-        if pd is None:
-            raise RuntimeError("Pandas could not be imported, "
-                               "cannot convert from pandas")
-
         if not isinstance(pdf, pd.core.frame.DataFrame):
-            raise Exception("pdf input is not a Pandas DataFrame")
+            raise TypeError("pdf input is not a Pandas DataFrame")
 
         gdf = cudf.DataFrame.from_pandas(pdf)
         self.from_cudf_edgelist(gdf, source=source, destination=destination,
@@ -318,10 +314,6 @@ def from_pandas_adjacency(self, pdf):
         pdf : pandas.DataFrame
             A DataFrame that contains adjacency information
         """
-        if pd is None:
-            raise RuntimeError("Pandas could not be imported, "
-                               "cannot convert from pandas")
-
         if not isinstance(pdf, pd.core.frame.DataFrame):
             raise TypeError("pdf input is not a Pandas DataFrame")
 
diff --git a/python/cugraph/cugraph/traversal/bfs.py b/python/cugraph/cugraph/traversal/bfs.py
index 0fb415de0c4..127ab3d0ba3 100644
--- a/python/cugraph/cugraph/traversal/bfs.py
+++ b/python/cugraph/cugraph/traversal/bfs.py
@@ -18,28 +18,10 @@
 from cugraph.utilities import (ensure_cugraph_obj,
                                is_matrix_type,
                                is_cp_matrix_type,
-                               import_optional,
+                               is_nx_graph_type,
+                               cupy_package as cp,
                                )
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
-cp = import_optional("cupy")
-cp_coo_matrix = import_optional("coo_matrix",
-                                import_from="cupyx.scipy.sparse.coo")
-cp_csr_matrix = import_optional("csr_matrix",
-                                import_from="cupyx.scipy.sparse.csr")
-cp_csc_matrix = import_optional("csc_matrix",
-                                import_from="cupyx.scipy.sparse.csc")
-
-sp = import_optional("scipy")
-sp_coo_matrix = import_optional("coo_matrix",
-                                import_from="scipy.sparse.coo")
-sp_csr_matrix = import_optional("csr_matrix",
-                                import_from="scipy.sparse.csr")
-sp_csc_matrix = import_optional("csc_matrix",
-                                import_from="scipy.sparse.csc")
-
 
 def _ensure_args(G, start, i_start, directed):
     """
@@ -55,8 +37,7 @@ def _ensure_args(G, start, i_start, directed):
 
     G_type = type(G)
     # Check for Graph-type inputs
-    if (G_type in [Graph, DiGraph]) or \
-       ((nx is not None) and (G_type in [nx.Graph, nx.DiGraph])):
+    if (G_type in [Graph, DiGraph]) or is_nx_graph_type(G_type):
         if directed is not None:
             raise TypeError("'directed' cannot be specified for a "
                             "Graph-type input")
@@ -76,7 +57,7 @@ def _convert_df_to_output_type(df, input_type):
     if input_type in [Graph, DiGraph]:
         return df
 
-    elif (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    elif is_nx_graph_type(input_type):
         return df.to_pandas()
 
     elif is_matrix_type(input_type):
diff --git a/python/cugraph/cugraph/traversal/sssp.py b/python/cugraph/cugraph/traversal/sssp.py
index f3aebaf43bf..5d7041a4287 100644
--- a/python/cugraph/cugraph/traversal/sssp.py
+++ b/python/cugraph/cugraph/traversal/sssp.py
@@ -19,28 +19,10 @@
 from cugraph.utilities import (ensure_cugraph_obj,
                                is_matrix_type,
                                is_cp_matrix_type,
-                               import_optional,
+                               is_nx_graph_type,
+                               cupy_package as cp,
                                )
 
-# optional dependencies used for handling different input types
-nx = import_optional("networkx")
-
-cp = import_optional("cupy")
-cp_coo_matrix = import_optional("coo_matrix",
-                                import_from="cupyx.scipy.sparse.coo")
-cp_csr_matrix = import_optional("csr_matrix",
-                                import_from="cupyx.scipy.sparse.csr")
-cp_csc_matrix = import_optional("csc_matrix",
-                                import_from="cupyx.scipy.sparse.csc")
-
-sp = import_optional("scipy")
-sp_coo_matrix = import_optional("coo_matrix",
-                                import_from="scipy.sparse.coo")
-sp_csr_matrix = import_optional("csr_matrix",
-                                import_from="scipy.sparse.csr")
-sp_csc_matrix = import_optional("csc_matrix",
-                                import_from="scipy.sparse.csc")
-
 
 def _ensure_args(G, source, method, directed,
                  return_predecessors, unweighted, overwrite, indices):
@@ -61,8 +43,7 @@ def _ensure_args(G, source, method, directed,
 
     G_type = type(G)
     # Check for Graph-type inputs
-    if (G_type in [Graph, DiGraph]) or \
-       ((nx is not None) and (G_type in [nx.Graph, nx.DiGraph])):
+    if (G_type in [Graph, DiGraph]) 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")
@@ -107,7 +88,7 @@ def _convert_df_to_output_type(df, input_type, return_predecessors):
     if input_type in [Graph, DiGraph, MultiGraph, MultiDiGraph]:
         return df
 
-    elif (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    elif is_nx_graph_type(input_type):
         return df.to_pandas()
 
     elif is_matrix_type(input_type):
diff --git a/python/cugraph/cugraph/tree/minimum_spanning_tree.py b/python/cugraph/cugraph/tree/minimum_spanning_tree.py
index 6a5f7b5bf38..eebc4f10b56 100644
--- a/python/cugraph/cugraph/tree/minimum_spanning_tree.py
+++ b/python/cugraph/cugraph/tree/minimum_spanning_tree.py
@@ -13,8 +13,9 @@
 
 from cugraph.tree import minimum_spanning_tree_wrapper
 from cugraph.structure.graph_classes import Graph
-from cugraph.utilities import check_nx_graph
-from cugraph.utilities import cugraph_to_nx
+from cugraph.utilities import (ensure_cugraph_obj_for_nx,
+                               cugraph_to_nx,
+                               )
 
 
 def _minimum_spanning_tree_subgraph(G):
@@ -88,7 +89,7 @@ def minimum_spanning_tree(
 
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True:
         mst = _minimum_spanning_tree_subgraph(G)
@@ -128,7 +129,7 @@ def maximum_spanning_tree(
 
     """
 
-    G, isNx = check_nx_graph(G)
+    G, isNx = ensure_cugraph_obj_for_nx(G)
 
     if isNx is True:
         mst = _maximum_spanning_tree_subgraph(G)
diff --git a/python/cugraph/cugraph/tree/minimum_spanning_tree_wrapper.pyx b/python/cugraph/cugraph/tree/minimum_spanning_tree_wrapper.pyx
index 3068b45418d..7e51a07a5c2 100644
--- a/python/cugraph/cugraph/tree/minimum_spanning_tree_wrapper.pyx
+++ b/python/cugraph/cugraph/tree/minimum_spanning_tree_wrapper.pyx
@@ -22,7 +22,9 @@ from cugraph.structure import graph_primtypes_wrapper
 from libc.stdint cimport uintptr_t
 
 import cudf
-import rmm
+
+# FIXME: these are transitive dependencies and are not currently listed in the
+# conda recipe. Either make them optional or add them to the conda recipe.
 import numpy as np
 import cupy as cp
 
diff --git a/python/cugraph/cugraph/utilities/__init__.py b/python/cugraph/cugraph/utilities/__init__.py
index 6dc23ff53b7..f868bef0e0d 100644
--- a/python/cugraph/cugraph/utilities/__init__.py
+++ b/python/cugraph/cugraph/utilities/__init__.py
@@ -14,16 +14,17 @@
 # from cugraph.utilities.grmat import grmat_gen
 # from cugraph.utilities.pointer_utils import device_of_gpu_pointer
 from cugraph.utilities.nx_factory import convert_from_nx
-from cugraph.utilities.nx_factory import check_nx_graph
 from cugraph.utilities.nx_factory import df_score_to_dictionary
 from cugraph.utilities.nx_factory import df_edge_score_to_dictionary
 from cugraph.utilities.nx_factory import cugraph_to_nx
-from cugraph.utilities.nx_factory import is_networkx_graph
 from cugraph.utilities.utils import (import_optional,
                                      ensure_cugraph_obj,
+                                     ensure_cugraph_obj_for_nx,
                                      is_matrix_type,
                                      is_cp_matrix_type,
                                      is_sp_matrix_type,
-                                     renumber_vertex_pair
+                                     is_nx_graph_type,
+                                     renumber_vertex_pair,
+                                     cupy_package,
                                      )
 from cugraph.utilities.path_retrieval import get_traversed_cost
diff --git a/python/cugraph/cugraph/utilities/nx_factory.py b/python/cugraph/cugraph/utilities/nx_factory.py
index 7eb97b30582..b3e4bae6010 100644
--- a/python/cugraph/cugraph/utilities/nx_factory.py
+++ b/python/cugraph/cugraph/utilities/nx_factory.py
@@ -11,23 +11,28 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+"""
+Utilities specific to NetworkX.
+
+NetworkX is required at runtime in order to call any of these functions, so
+ensure code using these utilities has done the proper checks prior to calling.
+"""
+
 import cugraph
 from .utils import import_optional
 from cudf import from_pandas
 import numpy as np
 
+# nx will be a MissingModule instance if NetworkX is not installed (any
+# attribute access on a MissingModule instance results in a RuntimeError).
 nx = import_optional("networkx")
 
-
 def convert_from_nx(nxG, weight=None):
     """
     weight, if given, is the string/name of the edge attr in nxG to use for
     weights in the resulting cugraph obj.  If nxG has no edge attributes,
     weight is ignored even if specified.
     """
-    if nx is None:
-        raise RuntimeError("NetworkX could not be imported, "
-                           "cannot convert nxG")
     if type(nxG) == nx.classes.graph.Graph:
         G = cugraph.Graph()
     elif type(nxG) == nx.classes.digraph.DiGraph:
@@ -69,40 +74,6 @@ def convert_from_nx(nxG, weight=None):
     return G
 
 
-def is_networkx_graph(G):
-    if nx is None:
-        raise RuntimeError("NetworkX could not be imported, cannot check G")
-    return isinstance(G, nx.classes.graph.Graph)
-
-
-def check_nx_graph(G, weight=None):
-    """
-    This is a convenience function that will ensure the proper graph type
-
-    Parameters
-    ----------
-    G : cudf.Graph or networkx.Graph
-    weight : str or None
-        which column to use for weight. Default is None
-
-    Returns
-    -------
-    G : cudf.Graph
-        returns a cugraph.Graph that is either the orginal input or
-        a conversion from NetworkX
-
-    is_nx : Boolean
-        indicates rather or not the Graph was converted
-    """
-
-    if nx is None:
-        raise RuntimeError("NetworkX could not be imported, cannot check G")
-    if isinstance(G, nx.classes.graph.Graph):
-        return convert_from_nx(G, weight), True
-    else:
-        return G, False
-
-
 def df_score_to_dictionary(df, k, v="vertex"):
     """
     Convert a dataframe to a dictionary
@@ -175,9 +146,6 @@ def df_edge_score_to_dictionary(df, k, src="src", dst="dst"):
 
 
 def cugraph_to_nx(G):
-    if nx is None:
-        raise RuntimeError("NetworkX could not be imported, cannot convert G")
-
     pdf = G.view_edge_list().to_pandas()
     num_col = len(pdf.columns)
 
diff --git a/python/cugraph/cugraph/utilities/utils.py b/python/cugraph/cugraph/utilities/utils.py
index e4e93513630..c9002bd4ae3 100644
--- a/python/cugraph/cugraph/utilities/utils.py
+++ b/python/cugraph/cugraph/utilities/utils.py
@@ -11,6 +11,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import importlib
+
 from numba import cuda
 
 import cudf
@@ -27,12 +29,14 @@
     from cupyx.scipy.sparse.csr import csr_matrix as cp_csr_matrix
     from cupyx.scipy.sparse.csc import csc_matrix as cp_csc_matrix
 
-    CP_MATRIX_TYPES = [cp_coo_matrix, cp_csr_matrix, cp_csc_matrix]
-    CP_COMPRESSED_MATRIX_TYPES = [cp_csr_matrix, cp_csc_matrix]
+    __cp_matrix_types = [cp_coo_matrix, cp_csr_matrix, cp_csc_matrix]
+    __cp_compressed_matrix_types = [cp_csr_matrix, cp_csc_matrix]
 except ModuleNotFoundError:
     cp = None
-    CP_MATRIX_TYPES = []
-    CP_COMPRESSED_MATRIX_TYPES = []
+    __cp_matrix_types = []
+    __cp_compressed_matrix_types = []
+
+cupy_package = cp
 
 try:
     import scipy as sp
@@ -40,17 +44,23 @@
     from scipy.sparse.csr import csr_matrix as sp_csr_matrix
     from scipy.sparse.csc import csc_matrix as sp_csc_matrix
 
-    SP_MATRIX_TYPES = [sp_coo_matrix, sp_csr_matrix, sp_csc_matrix]
-    SP_COMPRESSED_MATRIX_TYPES = [sp_csr_matrix, sp_csc_matrix]
+    __sp_matrix_types = [sp_coo_matrix, sp_csr_matrix, sp_csc_matrix]
+    __sp_compressed_matrix_types = [sp_csr_matrix, sp_csc_matrix]
 except ModuleNotFoundError:
     sp = None
-    SP_MATRIX_TYPES = []
-    SP_COMPRESSED_MATRIX_TYPES = []
+    __sp_matrix_types = []
+    __sp_compressed_matrix_types = []
+
+scipy_package = sp
 
 try:
     import networkx as nx
+    __nx_graph_types = [nx.Graph, nx.DiGraph]
 except ModuleNotFoundError:
     nx = None
+    __nx_graph_types = []
+
+nx_package = nx
 
 
 def get_traversed_path(df, id):
@@ -236,6 +246,7 @@ def get_device_memory_info():
 # |      Many NetworkX algorithms designed for weighted graphs use
 # |      an edge attribute (by default `weight`) to hold a numerical value.
 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
@@ -247,14 +258,13 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
     from cugraph.utilities.nx_factory import convert_from_nx
 
     input_type = type(obj)
-    if input_type in [Graph, DiGraph, MultiGraph, MultiDiGraph]:
+    if is_cugraph_graph_type(input_type):
         return (obj, input_type)
 
-    elif (nx is not None) and (input_type in [nx.Graph, nx.DiGraph]):
+    elif is_nx_graph_type(input_type):
         return (convert_from_nx(obj, weight=nx_weight_attr), input_type)
 
-    elif (input_type in CP_MATRIX_TYPES) or (input_type in SP_MATRIX_TYPES):
-
+    elif (input_type in __cp_matrix_types) or (input_type in __sp_matrix_types):
         if matrix_graph_type is None:
             matrix_graph_type = Graph
         elif matrix_graph_type not in [Graph, DiGraph]:
@@ -264,13 +274,13 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
             )
 
         if input_type in (
-            CP_COMPRESSED_MATRIX_TYPES + SP_COMPRESSED_MATRIX_TYPES
+            __cp_compressed_matrix_types + __sp_compressed_matrix_types
         ):
             coo = obj.tocoo(copy=False)
         else:
             coo = obj
 
-        if input_type in CP_MATRIX_TYPES:
+        if input_type in __cp_matrix_types:
             df = cudf.DataFrame(
                 {
                     "source": cp.ascontiguousarray(coo.row),
@@ -296,45 +306,50 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
         raise TypeError(f"obj of type {input_type} is not supported.")
 
 
+# FIXME: if G is a Nx type, the weight attribute is assumed to be "weight", if
+# set. An additional optional parameter for the weight attr name when accepting
+# Nx graphs may be needed.  From the Nx docs:
+# |      Many NetworkX algorithms designed for weighted graphs use
+# |      an edge attribute (by default `weight`) to hold a numerical value.
+def ensure_cugraph_obj_for_nx(obj, nx_weight_attr=None):
+    """
+    Ensures a cuGraph Graph-type obj is returned for either cuGraph or Nx
+    Graph-type objs. If obj is a Nx type,
+    """
+    # FIXME: importing here to avoid circular import
+    from cugraph.utilities.nx_factory import convert_from_nx
+
+    input_type = type(obj)
+    if is_nx_graph_type(input_type):
+        return (convert_from_nx(obj, weight=nx_weight_attr), True)
+    elif is_cugraph_graph_type(input_type):
+        return (obj, False)
+    else:
+        raise TypeError("input must be either a cuGraph or NetworkX graph "
+                        f"type, got {input_type}")
+
+
 def is_cp_matrix_type(m):
-    return m in CP_MATRIX_TYPES
+    return m in __cp_matrix_types
 
 
 def is_sp_matrix_type(m):
-    return m in SP_MATRIX_TYPES
+    return m in __sp_matrix_types
 
 
 def is_matrix_type(m):
     return is_cp_matrix_type(m) or is_sp_matrix_type(m)
 
 
-def import_optional(mod, import_from=None):
-    """
-    import module or object 'mod', possibly from module 'import_from', and
-    return the module object or object.  If the import raises
-    ModuleNotFoundError, returns None.
+def is_nx_graph_type(g):
+    return g in __nx_graph_types
 
-    This method was written to support importing "optional" dependencies so
-    code can be written to run even if the dependency is not installed.
 
-    >>> nx = import_optional("networkx")  # networkx is not installed
-    >>> if nx:
-    ...    G = nx.Graph()
-    ... else:
-    ...    print("Warning: NetworkX is not installed, using CPUGraph")
-    ...    G = cpu_graph.Graph()
-    >>>
-    """
-    namespace = {}
-    try:
-        if import_from:
-            exec(f"from {import_from} import {mod}", namespace)
-        else:
-            exec(f"import {mod}", namespace)
-    except ModuleNotFoundError:
-        pass
+def is_cugraph_graph_type(g):
+    # FIXME: importing here to avoid circular import
+    from cugraph.structure import Graph, DiGraph, MultiGraph, MultiDiGraph
 
-    return namespace.get(mod)
+    return g in [Graph, DiGraph, MultiGraph, MultiDiGraph]
 
 
 def renumber_vertex_pair(input_graph, vertex_pair):
@@ -355,3 +370,64 @@ def renumber_vertex_pair(input_graph, vertex_pair):
                 vertex_pair, "dst", columns[vertex_size:]
             )
     return vertex_pair
+
+
+class MissingModule:
+    """
+    Raises RuntimeError when any attribute is accessed on instances of this
+    class.
+
+    Instances of this class are returned by import_optional() when a module
+    cannot be found, which allows for code to import optional dependencies, and
+    have only the code paths that use the module affected.
+    """
+    def __init__(self, mod_name):
+        self.name = mod_name
+
+    def __getattr__(self, attr):
+        raise RuntimeError(f"This feature requires the {self.name} package/module")
+
+
+def import_optional(mod, default_mod_class=MissingModule):
+    """
+    import the "optional" module 'mod' and return the module object or object.
+    If the import raises ModuleNotFoundError, returns an instance of
+    default_mod_class.
+
+    This method was written to support importing "optional" dependencies so
+    code can be written to run even if the dependency is not installed.
+
+    Example
+    -------
+    >>> nx = import_optional("networkx")  # networkx is not installed
+    >>> G = nx.Graph()
+    Traceback (most recent call last):
+      File "<stdin>", line 1, in <module>
+      ...
+    RuntimeError: This feature requires the networkx package/module
+
+    Example
+    -------
+    >>> class CuDFFallback:
+    ...   def __init__(self, mod_name):
+    ...     assert mod_name == "cudf"
+    ...     warnings.warn("cudf could not be imported, using pandas instead!")
+    ...   def __getattr__(self, attr):
+    ...     import pandas
+    ...     return getattr(pandas, attr)
+    ...
+    >>> df_mod = import_optional("cudf", default_mod_class=CuDFFallback)
+    <stdin>:4: UserWarning: cudf could not be imported, using pandas instead!
+    >>> df = df_mod.DataFrame()
+    >>> df
+    Empty DataFrame
+    Columns: []
+    Index: []
+    >>> type(df)
+    <class 'pandas.core.frame.DataFrame'>
+    >>>
+    """
+    try:
+        return importlib.import_module(mod)
+    except ModuleNotFoundError:
+        return default_mod_class(mod_name=mod)

From 52a9465b89c466492023c0d870533473ddd92a53 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <rratzel@nvidia.com>
Date: Mon, 27 Sep 2021 22:08:26 -0500
Subject: [PATCH 2/4] Fixed error with egonet where obj instead of type was
 being passed to type checking code, added docstrings for pylibcugraph.CC
 functions.

---
 python/cugraph/cugraph/community/egonet.py    |   2 +-
 .../pylibcugraph/components/_connectivity.pyx | 140 +++++++++++++++++-
 2 files changed, 137 insertions(+), 5 deletions(-)

diff --git a/python/cugraph/cugraph/community/egonet.py b/python/cugraph/cugraph/community/egonet.py
index 2fcafa5795b..09b7b419a15 100644
--- a/python/cugraph/cugraph/community/egonet.py
+++ b/python/cugraph/cugraph/community/egonet.py
@@ -25,7 +25,7 @@ def _convert_graph_to_output_type(G, input_type):
     Given a cugraph.Graph, convert it to a new type appropriate for the
     graph algos in this module, based on input_type.
     """
-    if is_nx_graph_type(G):
+    if is_nx_graph_type(input_type):
         return cugraph_to_nx(G)
 
     else:
diff --git a/python/pylibcugraph/pylibcugraph/components/_connectivity.pyx b/python/pylibcugraph/pylibcugraph/components/_connectivity.pyx
index 969feb199d7..89c1cb6c0d5 100644
--- a/python/pylibcugraph/pylibcugraph/components/_connectivity.pyx
+++ b/python/pylibcugraph/pylibcugraph/components/_connectivity.pyx
@@ -40,8 +40,74 @@ def _ensure_arg_types(**kwargs):
 
 def strongly_connected_components(offsets, indices, weights, num_verts, num_edges, labels):
     """
-    This is the docstring for strongly_connected_components
-    FIXME: write this docstring
+    Generate the Strongly Connected Components and attach a component label to
+    each vertex.
+
+    Parameters
+    ----------
+    offsets : object supporting a __cuda_array_interface__ interface
+        Array containing the offsets values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+    indices : object supporting a __cuda_array_interface__ interface
+        Array containing the indices values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+    weights : object supporting a __cuda_array_interface__ interface
+        Array containing the weights values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+        NOTE: weighted graphs are currently unsupported, and because of this the
+        weights parameter can only be set to None.
+
+    num_verts : int
+        The number of vertices present in the graph represented by the CSR
+        arrays above.
+
+    num_edges : int
+        The number of edges present in the graph represented by the CSR arrays
+        above.
+
+    labels : object supporting a __cuda_array_interface__ interface
+        Array of size num_verts that will be populated with component label
+        values. The component lables in the array are ordered based on the
+        sorted vertex ID values of the graph.  For example, labels [9, 9, 7]
+        mean vertex 0 is labelled 9, vertex 1 is labelled 9, and vertex 2 is
+        labelled 7.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> import numpy as np
+    >>> from scipy.sparse import csr_matrix
+    >>>
+    >>> graph = [
+    ... [0, 1, 1, 0, 0],
+    ... [0, 0, 1, 0, 0],
+    ... [0, 0, 0, 0, 0],
+    ... [0, 0, 0, 0, 1],
+    ... [0, 0, 0, 0, 0],
+    ... ]
+    >>> scipy_csr = csr_matrix(graph)
+    >>> num_verts = scipy_csr.get_shape()[0]
+    >>> num_edges = scipy_csr.nnz
+    >>>
+    >>> cp_offsets = cp.asarray(scipy_csr.indptr)
+    >>> cp_indices = cp.asarray(scipy_csr.indices, dtype=np.int32)
+    >>> cp_labels = cp.asarray(np.zeros(num_verts, dtype=np.int32))
+    >>>
+    >>> strongly_connected_components(offsets=cp_offsets,
+    ...                               indices=cp_indices,
+    ...                               weights=None,
+    ...                               num_verts=num_verts,
+    ...                               num_edges=num_edges,
+    ...                               labels=cp_labels)
+    >>> print(f"{len(set(cp_labels.tolist()))} - {cp_labels}")
+    5 - [0 1 2 3 4]
     """
     _ensure_arg_types(offsets=offsets, indices=indices,
                       weights=weights, labels=labels)
@@ -65,8 +131,74 @@ def strongly_connected_components(offsets, indices, weights, num_verts, num_edge
 
 def weakly_connected_components(offsets, indices, weights, num_verts, num_edges, labels):
     """
-    This is the docstring for weakly_connected_components
-    FIXME: write this docstring
+    Generate the Weakly Connected Components and attach a component label to
+    each vertex.
+
+    Parameters
+    ----------
+    offsets : object supporting a __cuda_array_interface__ interface
+        Array containing the offsets values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+    indices : object supporting a __cuda_array_interface__ interface
+        Array containing the indices values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+    weights : object supporting a __cuda_array_interface__ interface
+        Array containing the weights values of a Compressed Sparse Row matrix
+        that represents the graph.
+
+        NOTE: weighted graphs are currently unsupported, and because of this the
+        weights parameter can only be set to None.
+
+    num_verts : int
+        The number of vertices present in the graph represented by the CSR
+        arrays above.
+
+    num_edges : int
+        The number of edges present in the graph represented by the CSR arrays
+        above.
+
+    labels : object supporting a __cuda_array_interface__ interface
+        Array of size num_verts that will be populated with component label
+        values. The component lables in the array are ordered based on the
+        sorted vertex ID values of the graph.  For example, labels [9, 9, 7]
+        mean vertex 0 is labelled 9, vertex 1 is labelled 9, and vertex 2 is
+        labelled 7.
+
+    Returns
+    -------
+    None
+
+    Examples
+    --------
+    >>> import cupy as cp
+    >>> import numpy as np
+    >>> from scipy.sparse import csr_matrix
+    >>>
+    >>> graph = [
+    ... [0, 1, 1, 0, 0],
+    ... [0, 0, 1, 0, 0],
+    ... [0, 0, 0, 0, 0],
+    ... [0, 0, 0, 0, 1],
+    ... [0, 0, 0, 0, 0],
+    ... ]
+    >>> scipy_csr = csr_matrix(graph)
+    >>> num_verts = scipy_csr.get_shape()[0]
+    >>> num_edges = scipy_csr.nnz
+    >>>
+    >>> cp_offsets = cp.asarray(scipy_csr.indptr)
+    >>> cp_indices = cp.asarray(scipy_csr.indices, dtype=np.int32)
+    >>> cp_labels = cp.asarray(np.zeros(num_verts, dtype=np.int32))
+    >>>
+    >>> weakly_connected_components(offsets=cp_offsets,
+    ...                             indices=cp_indices,
+    ...                             weights=None,
+    ...                             num_verts=num_verts,
+    ...                             num_edges=num_edges,
+    ...                             labels=cp_labels)
+    >>> print(f"{len(set(cp_labels.tolist()))} - {cp_labels}")
+    2 - [1 1 1 4 4]
     """
     _ensure_arg_types(offsets=offsets, indices=indices,
                       weights=weights, labels=labels)

From 42c536d2e9312e32c4092ab1bb21e279edf120fc Mon Sep 17 00:00:00 2001
From: Rick Ratzel <rratzel@nvidia.com>
Date: Mon, 27 Sep 2021 22:28:25 -0500
Subject: [PATCH 3/4] flake8 fixes.

---
 python/cugraph/cugraph/centrality/katz_centrality.py | 1 -
 python/cugraph/cugraph/link_analysis/pagerank.py     | 2 +-
 python/cugraph/cugraph/utilities/nx_factory.py       | 1 +
 python/cugraph/cugraph/utilities/utils.py            | 8 +++++---
 4 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/python/cugraph/cugraph/centrality/katz_centrality.py b/python/cugraph/cugraph/centrality/katz_centrality.py
index af6c73dc366..de8a9339a55 100644
--- a/python/cugraph/cugraph/centrality/katz_centrality.py
+++ b/python/cugraph/cugraph/centrality/katz_centrality.py
@@ -15,7 +15,6 @@
 from cugraph.utilities import (ensure_cugraph_obj_for_nx,
                                df_score_to_dictionary,
                                )
-import cugraph
 
 
 def katz_centrality(
diff --git a/python/cugraph/cugraph/link_analysis/pagerank.py b/python/cugraph/cugraph/link_analysis/pagerank.py
index 6bb2fcff214..d6b0b65802c 100644
--- a/python/cugraph/cugraph/link_analysis/pagerank.py
+++ b/python/cugraph/cugraph/link_analysis/pagerank.py
@@ -16,7 +16,7 @@
 from cugraph.link_analysis import pagerank_wrapper
 from cugraph.utilities import (ensure_cugraph_obj_for_nx,
                                df_score_to_dictionary,
-                              )
+                               )
 
 
 def pagerank(
diff --git a/python/cugraph/cugraph/utilities/nx_factory.py b/python/cugraph/cugraph/utilities/nx_factory.py
index b3e4bae6010..c5d8ee22f83 100644
--- a/python/cugraph/cugraph/utilities/nx_factory.py
+++ b/python/cugraph/cugraph/utilities/nx_factory.py
@@ -27,6 +27,7 @@
 # attribute access on a MissingModule instance results in a RuntimeError).
 nx = import_optional("networkx")
 
+
 def convert_from_nx(nxG, weight=None):
     """
     weight, if given, is the string/name of the edge attr in nxG to use for
diff --git a/python/cugraph/cugraph/utilities/utils.py b/python/cugraph/cugraph/utilities/utils.py
index c9002bd4ae3..42db0a6b370 100644
--- a/python/cugraph/cugraph/utilities/utils.py
+++ b/python/cugraph/cugraph/utilities/utils.py
@@ -254,7 +254,7 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
     cugraph Graph-type obj to create when converting from a matrix type.
     """
     # FIXME: importing here to avoid circular import
-    from cugraph.structure import Graph, DiGraph, MultiGraph, MultiDiGraph
+    from cugraph.structure import Graph, DiGraph
     from cugraph.utilities.nx_factory import convert_from_nx
 
     input_type = type(obj)
@@ -264,7 +264,8 @@ def ensure_cugraph_obj(obj, nx_weight_attr=None, matrix_graph_type=None):
     elif is_nx_graph_type(input_type):
         return (convert_from_nx(obj, weight=nx_weight_attr), input_type)
 
-    elif (input_type in __cp_matrix_types) or (input_type in __sp_matrix_types):
+    elif (input_type in __cp_matrix_types) or \
+         (input_type in __sp_matrix_types):
         if matrix_graph_type is None:
             matrix_graph_type = Graph
         elif matrix_graph_type not in [Graph, DiGraph]:
@@ -385,7 +386,8 @@ def __init__(self, mod_name):
         self.name = mod_name
 
     def __getattr__(self, attr):
-        raise RuntimeError(f"This feature requires the {self.name} package/module")
+        raise RuntimeError(f"This feature requires the {self.name} "
+                           "package/module")
 
 
 def import_optional(mod, default_mod_class=MissingModule):

From db93b6322c971b431b541211022fd1712ee5f267 Mon Sep 17 00:00:00 2001
From: Rick Ratzel <rratzel@nvidia.com>
Date: Tue, 28 Sep 2021 08:54:30 -0500
Subject: [PATCH 4/4] Added FIXME for RW return type conversion, changed
 formatting of import in BC for consistency.

---
 .../cugraph/cugraph/centrality/betweenness_centrality.py   | 7 ++++---
 python/cugraph/cugraph/sampling/random_walks.py            | 5 +++++
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/python/cugraph/cugraph/centrality/betweenness_centrality.py b/python/cugraph/cugraph/centrality/betweenness_centrality.py
index 1dc79e39eb5..d86349504da 100644
--- a/python/cugraph/cugraph/centrality/betweenness_centrality.py
+++ b/python/cugraph/cugraph/centrality/betweenness_centrality.py
@@ -16,9 +16,10 @@
 import cudf
 from cugraph.centrality import betweenness_centrality_wrapper
 from cugraph.centrality import edge_betweenness_centrality_wrapper
-from cugraph.utilities import df_edge_score_to_dictionary
-from cugraph.utilities import df_score_to_dictionary
-from cugraph.utilities import ensure_cugraph_obj_for_nx
+from cugraph.utilities import (df_edge_score_to_dictionary,
+                               df_score_to_dictionary,
+                               ensure_cugraph_obj_for_nx,
+                               )
 import cugraph
 
 
diff --git a/python/cugraph/cugraph/sampling/random_walks.py b/python/cugraph/cugraph/sampling/random_walks.py
index 69ea6733f6a..8f7ad3fc3a8 100644
--- a/python/cugraph/cugraph/sampling/random_walks.py
+++ b/python/cugraph/cugraph/sampling/random_walks.py
@@ -57,6 +57,11 @@ def random_walks(G,
     if max_depth is None:
         raise TypeError("must specify a 'max_depth'")
 
+    # FIXME: supporting Nx types should mean having a return type that better
+    # matches Nx expectations (eg. data on the CPU, possibly using a different
+    # data struct like a dictionary, etc.). The 2nd value is ignored here,
+    # which is typically named isNx and used to convert the return type.
+    # Consider a different return type if Nx types are passed in.
     G, _ = ensure_cugraph_obj_for_nx(G)
 
     if start_vertices is int: