Add many graph generators to nx-cugraph

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -29,23 +29,67 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
+        "barbell_graph",
         "betweenness_centrality",
+        "bull_graph",
+        "caveman_graph",
+        "chvatal_graph",
+        "circular_ladder_graph",
+        "complete_bipartite_graph",
+        "complete_graph",
+        "complete_multipartite_graph",
+        "cubical_graph",
+        "cycle_graph",
+        "davis_southern_women_graph",
         "degree_centrality",
+        "desargues_graph",
+        "diamond_graph",
+        "dodecahedral_graph",
         "edge_betweenness_centrality",
+        "empty_graph",
+        "florentine_families_graph",
+        "from_pandas_edgelist",
+        "from_scipy_sparse_array",
+        "frucht_graph",
+        "heawood_graph",
+        "house_graph",
+        "house_x_graph",
+        "icosahedral_graph",
         "in_degree_centrality",
         "is_isolate",
         "isolates",
         "k_truss",
+        "karate_club_graph",
+        "krackhardt_kite_graph",
+        "ladder_graph",
+        "les_miserables_graph",
+        "lollipop_graph",
         "louvain_communities",
+        "moebius_kantor_graph",
+        "null_graph",
         "number_of_isolates",
         "number_of_selfloops",
+        "octahedral_graph",
         "out_degree_centrality",
+        "pappus_graph",
+        "path_graph",
+        "petersen_graph",
+        "sedgewick_maze_graph",
+        "star_graph",
+        "tetrahedral_graph",
+        "trivial_graph",
+        "truncated_cube_graph",
+        "truncated_tetrahedron_graph",
+        "turan_graph",
+        "tutte_graph",
+        "wheel_graph",
         # END: functions
     },
     "extra_docstrings": {
         # BEGIN: extra_docstrings
         "betweenness_centrality": "`weight` parameter is not yet supported.",
         "edge_betweenness_centrality": "`weight` parameter is not yet supported.",
+        "from_pandas_edgelist": "cudf.DataFrame inputs also supported.",
         "louvain_communities": "`seed` parameter is currently ignored.",
         # END: extra_docstrings
     },

python/nx-cugraph/lint.yaml

@@ -45,20 +45,20 @@ repos:
       - id: pyupgrade
         args: [--py39-plus]
   - repo: https://github.com/psf/black
-    rev: 23.10.0
+    rev: 23.10.1
     hooks:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.1
+    rev: v0.1.3
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]
   - repo: https://github.com/PyCQA/flake8
     rev: 6.1.0
     hooks:
       - id: flake8
-        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501']  # Why is this necessary?
+        args: ['--per-file-ignores=_nx_cugraph/__init__.py:E501', '--extend-ignore=SIM105']  # Why is this necessary?
         additional_dependencies: &flake8_dependencies
           # These versions need updated manually
           - flake8==6.1.0
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.1.1
+    rev: v0.1.3
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

python/nx-cugraph/nx_cugraph/__init__.py

@@ -20,11 +20,11 @@
 from . import convert
 from .convert import *
 
-# from . import convert_matrix
-# from .convert_matrix import *
+from . import convert_matrix
+from .convert_matrix import *
 
-# from . import generators
-# from .generators import *
+from . import generators
+from .generators import *
 
 from . import algorithms
 from .algorithms import *

python/nx-cugraph/nx_cugraph/algorithms/__init__.py

@@ -10,7 +10,8 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-from . import centrality, community
+from . import bipartite, centrality, community
+from .bipartite import complete_bipartite_graph
 from .centrality import *
 from .core import *
 from .isolate import *

python/nx-cugraph/nx_cugraph/algorithms/bipartite/__init__.py

@@ -0,0 +1,13 @@
+# Copyright (c) 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .generators import *

python/nx-cugraph/nx_cugraph/algorithms/bipartite/generators.py

@@ -0,0 +1,62 @@
+# Copyright (c) 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
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from numbers import Integral
+
+import cupy as cp
+import networkx as nx
+import numpy as np
+
+from nx_cugraph.generators._utils import _create_using_class, _number_and_nodes
+from nx_cugraph.utils import index_dtype, networkx_algorithm, nodes_or_number
+
+__all__ = [
+    "complete_bipartite_graph",
+]
+
+
+@nodes_or_number([0, 1])
+@networkx_algorithm
+def complete_bipartite_graph(n1, n2, create_using=None):
+    graph_class, inplace = _create_using_class(create_using)
+    if graph_class.is_directed():
+        raise nx.NetworkXError("Directed Graph not supported")
+    orig_n1, unused_nodes1 = n1
+    orig_n2, unused_nodes2 = n2
+    n1, nodes1 = _number_and_nodes(n1)
+    n2, nodes2 = _number_and_nodes(n2)
+    all_indices = cp.indices((n1, n2), dtype=index_dtype)
+    indices0 = all_indices[0].ravel()
+    indices1 = all_indices[1].ravel() + n1
+    del all_indices
+    src_indices = cp.hstack((indices0, indices1))
+    dst_indices = cp.hstack((indices1, indices0))
+    bipartite = cp.zeros(n1 + n2, np.int8)
+    bipartite[n1:] = 1
+    if isinstance(orig_n1, Integral) and isinstance(orig_n2, Integral):
+        nodes = None
+    else:
+        nodes = list(range(n1)) if nodes1 is None else nodes1
+        nodes.extend(range(n2) if nodes2 is None else nodes2)
+        if len(set(nodes)) != len(nodes):
+            raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
+    G = graph_class.from_coo(
+        n1 + n2,
+        src_indices,
+        dst_indices,
+        node_values={"bipartite": bipartite},
+        id_to_key=nodes,
+        name=f"complete_bipartite_graph({orig_n1}, {orig_n2})",
+    )
+    if inplace:
+        return create_using._become(G)
+    return G

python/nx-cugraph/nx_cugraph/algorithms/core.py

@@ -12,11 +12,10 @@
 # limitations under the License.
 import cupy as cp
 import networkx as nx
-import numpy as np
 import pylibcugraph as plc
 
 import nx_cugraph as nxcg
-from nx_cugraph.utils import networkx_algorithm, not_implemented_for
+from nx_cugraph.utils import _get_int_dtype, networkx_algorithm, not_implemented_for
 
 __all__ = ["k_truss"]
 
@@ -51,8 +50,8 @@ def k_truss(G, k):
         edge_values = {key: val.copy() for key, val in G.edge_values.items()}
         edge_masks = {key: val.copy() for key, val in G.edge_masks.items()}
     else:
-        # int dtype for edge_indices would be preferred
-        edge_indices = cp.arange(G.src_indices.size, dtype=np.float64)
+        edge_dtype = _get_int_dtype(G.src_indices.size - 1)
+        edge_indices = cp.arange(G.src_indices.size, dtype=edge_dtype)
         src_indices, dst_indices, edge_indices, _ = plc.k_truss_subgraph(
             resource_handle=plc.ResourceHandle(),
             graph=G._get_plc_graph(edge_array=edge_indices),
@@ -62,7 +61,9 @@ def k_truss(G, k):
         # Renumber step 0: node indices
         node_indices = cp.unique(cp.concatenate([src_indices, dst_indices]))
         # Renumber step 1: edge values
-        edge_indices = edge_indices.astype(np.int64)
+        if edge_indices.dtype != edge_dtype:
+            # The returned edge_indices may have different dtype (and float)
+            edge_indices = edge_indices.astype(edge_dtype)
         edge_values = {key: val[edge_indices] for key, val in G.edge_values.items()}
         edge_masks = {key: val[edge_indices] for key, val in G.edge_masks.items()}
     # Renumber step 2: edge indices