nx-cugraph: indicate which plc algorithms are used

python/nx-cugraph/nx_cugraph/algorithms/centrality/betweenness.py

@@ -18,7 +18,7 @@
 __all__ = ["betweenness_centrality", "edge_betweenness_centrality"]
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="betweenness_centrality")
 def betweenness_centrality(
     G, k=None, normalized=True, weight=None, endpoints=False, seed=None
 ):
@@ -46,7 +46,7 @@ def _(G, k=None, normalized=True, weight=None, endpoints=False, seed=None):
     return weight is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="edge_betweenness_centrality")
 def edge_betweenness_centrality(G, k=None, normalized=True, weight=None, seed=None):
     """`weight` parameter is not yet supported."""
     if weight is not None:

python/nx-cugraph/nx_cugraph/algorithms/centrality/eigenvector.py

@@ -26,7 +26,7 @@
 
 
 @not_implemented_for("multigraph")
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(extra_params=_dtype_param, plc="eigenvector_centrality")
 def eigenvector_centrality(
     G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None
 ):

python/nx-cugraph/nx_cugraph/algorithms/centrality/katz.py

@@ -26,7 +26,7 @@
 
 
 @not_implemented_for("multigraph")
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(extra_params=_dtype_param, plc="katz_centrality")
 def katz_centrality(
     G,
     alpha=0.1,

python/nx-cugraph/nx_cugraph/algorithms/community/louvain.py

@@ -35,7 +35,8 @@
             "Upper limit of the number of macro-iterations (max: 500)."
         ),
         **_dtype_param,
-    }
+    },
+    plc="louvain",
 )
 def louvain_communities(
     G,

python/nx-cugraph/nx_cugraph/algorithms/components/connected.py

@@ -57,7 +57,7 @@ def _(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components")
 def connected_components(G):
     G = _to_undirected_graph(G)
     if G.src_indices.size == 0:
@@ -86,7 +86,7 @@ def connected_components(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components")
 def is_connected(G):
     G = _to_undirected_graph(G)
     if len(G) == 0:
@@ -110,7 +110,7 @@ def is_connected(G):
 
 
 @not_implemented_for("directed")
-@networkx_algorithm
+@networkx_algorithm(plc="weakly_connected_components")
 def node_connected_component(G, n):
     # We could also do plain BFS from n
     G = _to_undirected_graph(G)

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

@@ -27,7 +27,7 @@
 
 @not_implemented_for("directed")
 @not_implemented_for("multigraph")
-@networkx_algorithm
+@networkx_algorithm(plc="k_truss_subgraph")
 def k_truss(G, k):
     """
     Currently raises `NotImplementedError` for graphs with more than one connected

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

@@ -45,11 +45,11 @@ def _ancestors_and_descendants(G, source, *, is_ancestors):
     return G._nodearray_to_set(node_ids[mask])
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def descendants(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=False)
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def ancestors(G, source):
     return _ancestors_and_descendants(G, source, is_ancestors=True)

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/hits_alg.py

@@ -32,7 +32,8 @@
             "The edge attribute to use as the edge weight."
         ),
         **_dtype_param,
-    }
+    },
+    plc="hits",
 )
 def hits(
     G,

python/nx-cugraph/nx_cugraph/algorithms/link_analysis/pagerank_alg.py

@@ -26,7 +26,9 @@
 __all__ = ["pagerank"]
 
 
-@networkx_algorithm(extra_params=_dtype_param)
+@networkx_algorithm(
+    extra_params=_dtype_param, plc={"pagerank", "personalized_pagerank"}
+)
 def pagerank(
     G,
     alpha=0.85,
@@ -97,7 +99,7 @@ def pagerank(
 
 
 @pagerank._can_run
-def pagerank(
+def _(
     G,
     alpha=0.85,
     personalization=None,

python/nx-cugraph/nx_cugraph/algorithms/shortest_paths/unweighted.py

@@ -21,12 +21,12 @@
 __all__ = ["single_source_shortest_path_length", "single_target_shortest_path_length"]
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def single_source_shortest_path_length(G, source, cutoff=None):
     return _single_shortest_path_length(G, source, cutoff, "Source")
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def single_target_shortest_path_length(G, target, cutoff=None):
     return _single_shortest_path_length(G, target, cutoff, "Target")
 

python/nx-cugraph/nx_cugraph/algorithms/traversal/breadth_first_search.py

@@ -57,7 +57,7 @@ def _bfs(G, source, *, depth_limit=None, reverse=False):
     return distances[mask], predecessors[mask], node_ids[mask]
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def generic_bfs_edges(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
     """`neighbors` and `sort_neighbors` parameters are not yet supported."""
     return bfs_edges(source, depth_limit=depth_limit)
@@ -68,7 +68,7 @@ def _(G, source, neighbors=None, depth_limit=None, sort_neighbors=None):
     return neighbors is None and sort_neighbors is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def bfs_edges(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
     G = _check_G_and_source(G, source)
@@ -95,7 +95,7 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def bfs_tree(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
     G = _check_G_and_source(G, source)
@@ -149,7 +149,7 @@ def _(G, source, reverse=False, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def bfs_successors(G, source, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
     G = _check_G_and_source(G, source)
@@ -173,7 +173,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def bfs_layers(G, sources):
     G = _to_graph(G)
     if sources in G:
@@ -201,7 +201,7 @@ def bfs_layers(G, sources):
     return (G._nodearray_to_list(groups[key]) for key in range(len(groups)))
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def bfs_predecessors(G, source, depth_limit=None, sort_neighbors=None):
     """`sort_neighbors` parameter is not yet supported."""
     G = _check_G_and_source(G, source)
@@ -227,7 +227,7 @@ def _(G, source, depth_limit=None, sort_neighbors=None):
     return sort_neighbors is None
 
 
-@networkx_algorithm
+@networkx_algorithm(plc="bfs")
 def descendants_at_distance(G, source, distance):
     G = _check_G_and_source(G, source)
     if distance is None or distance < 0:

python/nx-cugraph/nx_cugraph/utils/decorators.py

@@ -41,6 +41,7 @@ class networkx_algorithm:
     name: str
     extra_doc: str | None
     extra_params: dict[str, str] | None
+    _plc_names: set[str] | None
 
     def __new__(
         cls,
@@ -49,13 +50,15 @@ def __new__(
         name: str | None = None,
         extra_params: dict[str, str] | str | None = None,
         nodes_or_number: list[int] | int | None = None,
+        plc: str | set[str] | None = None,
     ):
         if func is None:
             return partial(
                 networkx_algorithm,
                 name=name,
                 extra_params=extra_params,
                 nodes_or_number=nodes_or_number,
+                plc=plc,
             )
         instance = object.__new__(cls)
         if nodes_or_number is not None and nx.__version__[:3] > "3.2":
@@ -74,6 +77,12 @@ def __new__(
                 f"extra_params must be dict, str, or None; got {type(extra_params)}"
             )
         instance.extra_params = extra_params
+        if plc is None or isinstance(plc, set):
+            instance._plc_names = plc
+        elif isinstance(plc, str):
+            instance._plc_names = {plc}
+        else:
+            raise TypeError(f"plc argument must be str, set, or None; got {type(plc)}")
         # The docstring on our function is added to the NetworkX docstring.
         instance.extra_doc = (
             dedent(func.__doc__.lstrip("\n").rstrip()) if func.__doc__ else None
@@ -91,6 +100,11 @@ def __new__(
 
     def _can_run(self, func):
         """Set the `can_run` attribute to the decorated function."""
+        if not func.__name__.startswith("_"):
+            raise ValueError(
+                "The name of the function used by `_can_run` must begin with '_'; "
+                f"got: {func.__name__!r}"
+            )
         self.can_run = func
 
     def __call__(self, /, *args, **kwargs):