Add and fix a couple generators

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -37,6 +37,7 @@
         "circular_ladder_graph",
         "complete_bipartite_graph",
         "complete_graph",
+        "complete_multipartite_graph",
         "cubical_graph",
         "cycle_graph",
         "davis_southern_women_graph",
@@ -72,6 +73,7 @@
         "trivial_graph",
         "truncated_cube_graph",
         "truncated_tetrahedron_graph",
+        "turan_graph",
         "tutte_graph",
         "wheel_graph",
         # END: functions

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

@@ -10,6 +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 numbers import Integral
+
 import cupy as cp
 import networkx as nx
 import numpy as np
@@ -28,6 +30,8 @@ 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)
@@ -37,24 +41,21 @@ def complete_bipartite_graph(n1, n2, create_using=None):
     src_indices = cp.hstack((indices0, indices1))
     dst_indices = cp.hstack((indices1, indices0))
     bipartite = cp.zeros(n1 + n2, np.int8)
-    bipartite[n1 : n1 + n2] = 1
-    if nodes1 is None:
-        if nodes2 is None:
-            nodes = None
-        else:
-            nodes = list(range(n1))
-            nodes.extend(nodes2)
+    bipartite[n1:] = 1
+    if isinstance(orig_n1, Integral) and isinstance(orig_n2, Integral):
+        nodes = None
     else:
-        nodes = nodes1
-        nodes.extend(range(n1, n1 + n2) if nodes2 is None else nodes2)
-    if nodes is not None and len(set(nodes)) != len(nodes):
-        raise nx.NetworkXError("Inputs n1 and n2 must contain distinct nodes")
+        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},
-        name=f"complete_bipartite_graph({n1}, {n2})",
+        id_to_key=nodes,
+        name=f"complete_bipartite_graph({orig_n1}, {orig_n2})",
     )
     if inplace:
         return create_using._become(G)

python/nx-cugraph/nx_cugraph/convert.py

@@ -483,14 +483,17 @@ def _iter_attr_dicts(
     return full_dicts
 
 
-def to_networkx(G: nxcg.Graph) -> nx.Graph:
+def to_networkx(G: nxcg.Graph, *, sort_edges: bool = True) -> nx.Graph:
     """Convert a nx_cugraph graph to networkx graph.
 
     All edge and node attributes and ``G.graph`` properties are converted.
 
     Parameters
     ----------
     G : nx_cugraph.Graph
+    sort_edges : bool, default True
+        Whether to sort the edge data of the input graph by (src, dst) indices
+        before converting. This can be useful for consistent conversions.
 
     Returns
     -------
@@ -502,6 +505,8 @@ def to_networkx(G: nxcg.Graph) -> nx.Graph:
     """
     rv = G.to_networkx_class()()
     id_to_key = G.id_to_key
+    if sort_edges:
+        G._sort_edge_indices()
 
     node_values = G.node_values
     node_masks = G.node_masks

python/nx-cugraph/nx_cugraph/generators/_utils.py

@@ -80,12 +80,15 @@ def _create_using_class(create_using, *, default=nxcg.Graph):
         create_using, "is_multigraph"
     ):
         raise TypeError("create_using is not a valid graph type or instance")
+    elif not isinstance(create_using, nxcg.Graph):
+        raise NotImplementedError(
+            f"create_using with object of type {type(create_using)} is not supported "
+            "by the cugraph backend; only nx_cugraph.Graph objects are allowed."
+        )
     else:
-        if isinstance(create_using, nxcg.Graph):
-            create_using.clear()
-            inplace = True
-        # What does the user intend if they give us a graph object not from nx-cugraph?
+        inplace = True
         G = create_using
+        G.clear()
     if not isinstance(G, nxcg.Graph):
         if G.is_multigraph():
             if G.is_directed():
@@ -96,6 +99,12 @@ def _create_using_class(create_using, *, default=nxcg.Graph):
             graph_class = nxcg.DiGraph
         else:
             graph_class = nxcg.Graph
+        if G.__class__ not in {nx.Graph, nx.DiGraph, nx.MultiGraph, nx.MultiDiGraph}:
+            raise NotImplementedError(
+                f"create_using with type {type(G)} is not supported by the cugraph "
+                "backend; only standard networkx or nx_cugraph Graph objects are "
+                "allowed (but not customized subclasses derived from them)."
+            )
     else:
         graph_class = G.__class__
     return graph_class, inplace

python/nx-cugraph/nx_cugraph/generators/classic.py

@@ -10,14 +10,21 @@
 # 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.
+import itertools
+from numbers import Integral
+
 import cupy as cp
 import networkx as nx
+import numpy as np
+
+import nx_cugraph as nxcg
 
 from ..utils import index_dtype, networkx_algorithm, nodes_or_number
 from ._utils import (
     _common_small_graph,
     _complete_graph_indices,
     _create_using_class,
+    _ensure_int,
     _ensure_nonnegative_int,
     _number_and_nodes,
 )
@@ -26,6 +33,7 @@
     "barbell_graph",
     "circular_ladder_graph",
     "complete_graph",
+    "complete_multipartite_graph",
     "cycle_graph",
     "empty_graph",
     "ladder_graph",
@@ -34,9 +42,12 @@
     "path_graph",
     "star_graph",
     "trivial_graph",
+    "turan_graph",
     "wheel_graph",
 ]
 
+concat = itertools.chain.from_iterable
+
 
 @networkx_algorithm
 def barbell_graph(m1, m2, create_using=None):
@@ -87,6 +98,46 @@ def complete_graph(n, create_using=None):
     return G
 
 
+@networkx_algorithm
+def complete_multipartite_graph(*subset_sizes):
+    if not subset_sizes:
+        return nxcg.Graph()
+    try:
+        subset_sizes = [_ensure_int(size) for size in subset_sizes]
+    except TypeError:
+        subsets = [list(subset) for subset in subset_sizes]
+        subset_sizes = [len(subset) for subset in subsets]
+        nodes = list(concat(subsets))
+    else:
+        subsets = nodes = None
+        subset_sizes = [_ensure_nonnegative_int(size) for size in subset_sizes]
+    L1 = []
+    L2 = []
+    total = 0
+    for size in subset_sizes:
+        all_indices = cp.indices((total, size), dtype=index_dtype)
+        L1.append(all_indices[0].ravel())
+        L2.append(all_indices[1].ravel() + total)
+        total += size
+    src_indices = cp.hstack(L1 + L2)
+    dst_indices = cp.hstack(L2 + L1)
+    subsets_array = cp.array(
+        np.repeat(
+            np.arange(
+                len(subset_sizes), dtype=np.min_scalar_type(len(subset_sizes) - 1)
+            ),
+            subset_sizes,
+        )
+    )
+    return nxcg.Graph.from_coo(
+        subsets_array.size,
+        src_indices,
+        dst_indices,
+        node_values={"subset": subsets_array},
+        id_to_key=nodes,
+    )
+
+
 @nodes_or_number(0)
 @networkx_algorithm
 def cycle_graph(n, create_using=None):
@@ -190,6 +241,8 @@ def ladder_graph(n, create_using=None):
 @networkx_algorithm
 def lollipop_graph(m, n, create_using=None):
     # Like complete_graph then path_graph
+    orig_m, unused_nodes1 = m
+    orig_n, unused_nodes2 = n
     m, m_nodes = _number_and_nodes(m)
     if m < 2:
         raise nx.NetworkXError(
@@ -207,17 +260,13 @@ def lollipop_graph(m, n, create_using=None):
     ).ravel()[1:-1]
     src_indices = cp.hstack((msrc_indices, nsrc_indices))
     dst_indices = cp.hstack((mdst_indices, ndst_indices))
-    if m_nodes is None:
-        if n_nodes is None:
-            nodes = None
-        else:
-            nodes = list(range(m))
-            nodes.extend(n_nodes)
+    if isinstance(orig_m, Integral) and isinstance(orig_n, Integral):
+        nodes = None
     else:
-        nodes = m_nodes
-        nodes.extend(range(m, m + n) if n_nodes is None else n_nodes)
-    if nodes is not None and len(set(nodes)) != len(nodes):
-        raise nx.NetworkXError("Nodes must be distinct in containers m and n")
+        nodes = list(range(m)) if m_nodes is None else m_nodes
+        nodes.extend(range(n) if n_nodes is None else n_nodes)
+        if len(set(nodes)) != len(nodes):
+            raise nx.NetworkXError("Nodes must be distinct in containers m and n")
     G = graph_class.from_coo(m + n, src_indices, dst_indices, id_to_key=nodes)
     if inplace:
         return create_using._become(G)
@@ -253,19 +302,23 @@ def path_graph(n, create_using=None):
 @nodes_or_number(0)
 @networkx_algorithm
 def star_graph(n, create_using=None):
+    orig_n, orig_nodes = n
     n, nodes = _number_and_nodes(n)
-    if nodes is not None:
-        nodes.append(n)
-    if n < 2:
-        return _common_small_graph(n + 1, nodes, create_using, allow_directed=False)
+    # star_graph behaves differently whether the input was an int or iterable
+    if isinstance(orig_n, Integral):
+        if nodes is not None:
+            nodes.append(n)
+        n += 1
+    if n < 3:
+        return _common_small_graph(n, nodes, create_using, allow_directed=False)
     graph_class, inplace = _create_using_class(create_using)
     if graph_class.is_directed():
         raise nx.NetworkXError("Directed Graph not supported")
-    flat = cp.zeros(n, index_dtype)
-    ramp = cp.arange(1, n + 1, dtype=index_dtype)
+    flat = cp.zeros(n - 1, index_dtype)
+    ramp = cp.arange(1, n, dtype=index_dtype)
     src_indices = cp.hstack((flat, ramp))
     dst_indices = cp.hstack((ramp, flat))
-    G = graph_class.from_coo(n + 1, src_indices, dst_indices, id_to_key=nodes)
+    G = graph_class.from_coo(n, src_indices, dst_indices, id_to_key=nodes)
     if inplace:
         return create_using._become(G)
     return G
@@ -276,6 +329,15 @@ def trivial_graph(create_using=None):
     return _common_small_graph(1, None, create_using)
 
 
+@networkx_algorithm
+def turan_graph(n, r):
+    if not 1 <= r <= n:
+        raise nx.NetworkXError("Must satisfy 1 <= r <= n")
+    n_div_r, n_mod_r = divmod(n, r)
+    partitions = [n_div_r] * (r - n_mod_r) + [n_div_r + 1] * n_mod_r
+    return complete_multipartite_graph(*partitions)
+
+
 @nodes_or_number(0)
 @networkx_algorithm
 def wheel_graph(n, create_using=None):

python/nx-cugraph/nx_cugraph/generators/social.py

@@ -30,31 +30,31 @@ def davis_southern_women_graph():
     # fmt: off
     src_indices = cp.array(
         [
-            0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4,
-            4, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9,
-            9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 13, 13, 13,
-            14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 16, 16, 16, 16, 16, 16, 16,
-            16, 17, 17, 17, 17, 18, 18, 18, 19, 19, 19, 19, 19, 20, 20, 20, 20, 21,
-            21, 21, 21, 21, 21, 22, 22, 22, 23, 23, 23, 24, 24, 24, 25, 25, 25, 25,
-            25, 25, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28,
-            28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30,
-            30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31,
-            31, 31, 31, 31,
+            0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3,
+            3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 8, 8, 8,
+            8, 9, 9, 9, 9, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12,
+            12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 15, 15,
+            16, 16, 17, 17, 18, 18, 18, 19, 19, 19, 20, 20, 20, 20, 20, 20, 21, 21,
+            21, 21, 22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23,
+            24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, 25, 25, 25, 25,
+            25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26,
+            27, 27, 27, 27, 27, 28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 30, 30, 30,
+            31, 31, 31,
         ],
         index_dtype,
     )
     dst_indices = cp.array(
         [
-            18, 25, 26, 27, 28, 29, 30, 25, 26, 27, 29, 30, 31, 27, 28, 29, 30, 18,
-            24, 25, 26, 27, 28, 30, 31, 20, 31, 25, 27, 28, 30, 19, 20, 21, 29, 30,
-            19, 21, 22, 23, 30, 31, 18, 24, 25, 27, 28, 29, 30, 19, 21, 30, 31, 19,
-            20, 21, 22, 23, 28, 29, 31, 20, 31, 28, 30, 31, 27, 29, 30, 31, 19, 21,
-            22, 23, 29, 30, 31, 24, 25, 26, 27, 28, 29, 30, 31, 21, 29, 30, 31, 0, 4,
-            9, 7, 8, 10, 11, 15, 5, 7, 11, 12, 7, 8, 10, 11, 15, 17, 8, 11, 15, 8,
-            11, 15, 4, 9, 16, 0, 1, 4, 6, 9, 16, 0, 1, 4, 16, 0, 1, 3, 4, 6, 9, 14,
-            16, 0, 3, 4, 6, 9, 11, 13, 16, 0, 1, 3, 7, 9, 11, 14, 15, 16, 17, 0, 2,
-            3, 4, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 2, 4, 5, 8, 10, 11, 12, 13, 14,
-            15, 16, 17,
+            18, 19, 20, 21, 22, 23, 25, 26, 18, 19, 20, 22, 23, 24, 25, 19, 20, 21,
+            22, 23, 24, 25, 26, 18, 20, 21, 22, 23, 24, 25, 20, 21, 22, 24, 20, 22,
+            23, 25, 22, 23, 24, 25, 23, 25, 26, 22, 24, 25, 26, 24, 25, 26, 29, 25,
+            26, 27, 29, 25, 26, 27, 29, 30, 31, 24, 25, 26, 27, 29, 30, 31, 23, 24,
+            26, 27, 28, 29, 30, 31, 24, 25, 27, 28, 29, 25, 26, 26, 28, 26, 28, 0, 1,
+            3, 0, 1, 2, 0, 1, 2, 3, 4, 5, 0, 2, 3, 4, 0, 1, 2, 3, 4, 5, 6, 8, 0, 1,
+            2, 3, 5, 6, 7, 13, 1, 2, 3, 4, 6, 8, 9, 12, 13, 14, 0, 1, 2, 3, 5, 6, 7,
+            8, 9, 10, 11, 12, 14, 15, 0, 2, 7, 8, 9, 10, 11, 12, 13, 15, 16, 17, 10,
+            11, 12, 13, 14, 13, 14, 16, 17, 9, 10, 11, 12, 13, 14, 11, 12, 13, 11,
+            12, 13,
         ],
         index_dtype,
     )
@@ -65,14 +65,6 @@ def davis_southern_women_graph():
         ],
         np.int8,
     )
-    nodes = [
-        "Brenda Rogers", "Charlotte McDowd", "Dorothy Murchison", "Eleanor Nye",
-        "Evelyn Jefferson", "Flora Price", "Frances Anderson", "Helen Lloyd",
-        "Katherina Rogers", "Laura Mandeville", "Myra Liddel", "Nora Fayette",
-        "Olivia Carleton", "Pearl Oglethorpe", "Ruth DeSand", "Sylvia Avondale",
-        "Theresa Anderson", "Verne Sanderson", "E1", "E10", "E11", "E12", "E13",
-        "E14", "E2", "E3", "E4", "E5", "E6", "E7", "E8", "E9",
-    ]
     women = [
         "Evelyn Jefferson", "Laura Mandeville", "Theresa Anderson", "Brenda Rogers",
         "Charlotte McDowd", "Frances Anderson", "Eleanor Nye", "Pearl Oglethorpe",
@@ -90,7 +82,7 @@ def davis_southern_women_graph():
         src_indices,
         dst_indices,
         node_values={"bipartite": bipartite},
-        id_to_key=nodes,
+        id_to_key=women + events,
         top=women,
         bottom=events,
     )