update docstrings and init file

python/cugraph/cugraph/__init__.py

@@ -110,6 +110,7 @@
     random_walks,
     uniform_random_walks,
     biased_random_walks,
+    node2vec_random_walks,
     rw_path,
     node2vec,
     uniform_neighbor_sample,

python/cugraph/cugraph/dask/__init__.py

@@ -28,6 +28,8 @@
 from .components.connectivity import weakly_connected_components
 from .sampling.uniform_neighbor_sample import uniform_neighbor_sample
 from .sampling.random_walks import random_walks
+from .sampling.uniform_random_walks import uniform_random_walks
+from .sampling.biased_random_walks import biased_random_walks
 from .sampling.node2vec_random_walks import node2vec_random_walks
 from .centrality.eigenvector_centrality import eigenvector_centrality
 from .cores.core_number import core_number

python/cugraph/cugraph/dask/sampling/node2vec_random_walks.py

@@ -71,8 +71,9 @@ def node2vec_random_walks(
     random_state=None
 ):
     """
-    Computes random walks for each node in 'start_vertices', under the
-    node2vec sampling framework.
+    compute random walks under the node2vec sampling framework for each nodes in
+    'start_vertices' and returns a padded result along with the maximum path length.
+    Vertices with no outgoing edges will be padded with -1.
 
     parameters
     ----------
@@ -112,6 +113,11 @@ def node2vec_random_walks(
     edge_weight_paths: dask_cudf.Series
         Series containing the edge weights of edges represented by the
         returned vertex_paths
+
+    and
+
+    max_path_length : int
+        The maximum path length.
     """
     client = default_client()
 
@@ -194,5 +200,5 @@ def node2vec_random_walks(
         ]
     )
 
-    return ddf_vertex_paths, ddf_edge_wgt_paths
+    return ddf_vertex_paths, ddf_edge_wgt_paths, max_depth
 

python/cugraph/cugraph/sampling/__init__.py

@@ -14,5 +14,6 @@
 from cugraph.sampling.random_walks import random_walks, rw_path
 from cugraph.sampling.uniform_random_walks import uniform_random_walks
 from cugraph.sampling.biased_random_walks import biased_random_walks
+from cugraph.sampling.node2vec_random_walks import node2vec_random_walks
 from cugraph.sampling.node2vec import node2vec
 from cugraph.sampling.uniform_neighbor_sample import uniform_neighbor_sample