nx-cugraph: add more shortest path algorithms

python/nx-cugraph/README.md

@@ -95,8 +95,6 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
 
 <pre>
 <a href="https://networkx.org/documentation/stable/reference/algorithms/bipartite.html#module-networkx.algorithms.bipartite">bipartite</a>
- ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/bipartite.html#module-networkx.algorithms.bipartite.basic">basic</a>
- │   └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.bipartite.basic.is_bipartite.html#networkx.algorithms.bipartite.basic.is_bipartite">is_bipartite</a>
  └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/bipartite.html#module-networkx.algorithms.bipartite.generators">generators</a>
      └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.bipartite.generators.complete_bipartite_graph.html#networkx.algorithms.bipartite.generators.complete_bipartite_graph">complete_bipartite_graph</a>
 <a href="https://networkx.org/documentation/stable/reference/algorithms/centrality.html#module-networkx.algorithms.centrality">centrality</a>
@@ -152,9 +150,26 @@ Below is the list of algorithms that are currently supported in nx-cugraph.
  ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.overall_reciprocity.html#networkx.algorithms.reciprocity.overall_reciprocity">overall_reciprocity</a>
  └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.reciprocity.reciprocity.html#networkx.algorithms.reciprocity.reciprocity">reciprocity</a>
 <a href="https://networkx.org/documentation/stable/reference/algorithms/shortest_paths.html">shortest_paths</a>
- └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/shortest_paths.html#module-networkx.algorithms.shortest_paths.unweighted">unweighted</a>
-     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length">single_source_shortest_path_length</a>
-     └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length">single_target_shortest_path_length</a>
+ ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/shortest_paths.html#module-networkx.algorithms.shortest_paths.generic">generic</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.has_path.html#networkx.algorithms.shortest_paths.generic.has_path">has_path</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.shortest_path.html#networkx.algorithms.shortest_paths.generic.shortest_path">shortest_path</a>
+ │   └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.generic.shortest_path_length.html#networkx.algorithms.shortest_paths.generic.shortest_path_length">shortest_path_length</a>
+ ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/shortest_paths.html#module-networkx.algorithms.shortest_paths.unweighted">unweighted</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path">all_pairs_shortest_path</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.all_pairs_shortest_path_length">all_pairs_shortest_path_length</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.bidirectional_shortest_path">bidirectional_shortest_path</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path">single_source_shortest_path</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_source_shortest_path_length">single_source_shortest_path_length</a>
+ │   ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path.html#networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path">single_target_shortest_path</a>
+ │   └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length.html#networkx.algorithms.shortest_paths.unweighted.single_target_shortest_path_length">single_target_shortest_path_length</a>
+ └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/shortest_paths.html#module-networkx.algorithms.shortest_paths.weighted">weighted</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path">all_pairs_bellman_ford_path</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.all_pairs_bellman_ford_path_length">all_pairs_bellman_ford_path_length</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.bellman_ford_path">bellman_ford_path</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.bellman_ford_path_length">bellman_ford_path_length</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford">single_source_bellman_ford</a>
+     ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path">single_source_bellman_ford_path</a>
+     └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length.html#networkx.algorithms.shortest_paths.weighted.single_source_bellman_ford_path_length">single_source_bellman_ford_path_length</a>
 <a href="https://networkx.org/documentation/stable/reference/algorithms/traversal.html">traversal</a>
  └─ <a href="https://networkx.org/documentation/stable/reference/algorithms/traversal.html#module-networkx.algorithms.traversal.breadth_first_search">breadth_first_search</a>
      ├─ <a href="https://networkx.org/documentation/stable/reference/algorithms/generated/networkx.algorithms.traversal.breadth_first_search.bfs_edges.html#networkx.algorithms.traversal.breadth_first_search.bfs_edges">bfs_edges</a>

python/nx-cugraph/_nx_cugraph/__init__.py

@@ -33,15 +33,22 @@
     # "description": "TODO",
     "functions": {
         # BEGIN: functions
+        "all_pairs_bellman_ford_path",
+        "all_pairs_bellman_ford_path_length",
+        "all_pairs_shortest_path",
+        "all_pairs_shortest_path_length",
         "ancestors",
         "average_clustering",
         "barbell_graph",
+        "bellman_ford_path",
+        "bellman_ford_path_length",
         "betweenness_centrality",
         "bfs_edges",
         "bfs_layers",
         "bfs_predecessors",
         "bfs_successors",
         "bfs_tree",
+        "bidirectional_shortest_path",
         "bull_graph",
         "caveman_graph",
         "chvatal_graph",
@@ -70,14 +77,14 @@
         "from_scipy_sparse_array",
         "frucht_graph",
         "generic_bfs_edges",
+        "has_path",
         "heawood_graph",
         "hits",
         "house_graph",
         "house_x_graph",
         "icosahedral_graph",
         "in_degree_centrality",
         "is_arborescence",
-        "is_bipartite",
         "is_branching",
         "is_connected",
         "is_forest",
@@ -110,7 +117,14 @@
         "reciprocity",
         "reverse",
         "sedgewick_maze_graph",
+        "shortest_path",
+        "shortest_path_length",
+        "single_source_bellman_ford",
+        "single_source_bellman_ford_path",
+        "single_source_bellman_ford_path_length",
+        "single_source_shortest_path",
         "single_source_shortest_path_length",
+        "single_target_shortest_path",
         "single_target_shortest_path_length",
         "star_graph",
         "tadpole_graph",
@@ -128,7 +142,11 @@
     },
     "additional_docs": {
         # BEGIN: additional_docs
+        "all_pairs_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "all_pairs_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "average_clustering": "Directed graphs and `weight` parameter are not yet supported.",
+        "bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "betweenness_centrality": "`weight` parameter is not yet supported, and RNG with seed may be different.",
         "bfs_edges": "`sort_neighbors` parameter is not yet supported.",
         "bfs_predecessors": "`sort_neighbors` parameter is not yet supported.",
@@ -147,11 +165,28 @@
         "katz_centrality": "`nstart` isn't used (but is checked), and `normalized=False` is not supported.",
         "louvain_communities": "`seed` parameter is currently ignored, and self-loops are not yet supported.",
         "pagerank": "`dangling` parameter is not supported, but it is checked for validity.",
+        "shortest_path": "Negative weights are not yet supported, and method is ununsed.",
+        "shortest_path_length": "Negative weights are not yet supported, and method is ununsed.",
+        "single_source_bellman_ford": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
+        "single_source_bellman_ford_path_length": "Negative cycles are not yet supported. ``NotImplementedError`` will be raised if there are negative edge weights. We plan to support negative edge weights soon. Also, callable ``weight`` argument is not supported.",
         "transitivity": "Directed graphs are not yet supported.",
         # END: additional_docs
     },
     "additional_parameters": {
         # BEGIN: additional_parameters
+        "all_pairs_bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "all_pairs_bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         "eigenvector_centrality": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
@@ -169,6 +204,21 @@
         "pagerank": {
             "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
         },
+        "shortest_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "shortest_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford_path": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
+        "single_source_bellman_ford_path_length": {
+            "dtype : dtype or None, optional": "The data type (np.float32, np.float64, or None) to use for the edge weights in the algorithm. If None, then dtype is determined by the edge values.",
+        },
         # END: additional_parameters
     },
 }

python/nx-cugraph/lint.yaml

@@ -50,7 +50,7 @@ repos:
       - id: black
       # - id: black-jupyter
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
       - id: ruff
         args: [--fix-only, --show-fixes]  # --unsafe-fixes]
@@ -77,7 +77,7 @@ repos:
         additional_dependencies: [tomli]
         files: ^(nx_cugraph|docs)/
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.2.2
+    rev: v0.3.2
     hooks:
       - id: ruff
   - repo: https://github.com/pre-commit/pre-commit-hooks

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

@@ -22,7 +22,7 @@
     traversal,
     tree,
 )
-from .bipartite import complete_bipartite_graph, is_bipartite
+from .bipartite import complete_bipartite_graph
 from .centrality import *
 from .cluster import *
 from .components import *

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

@@ -10,5 +10,4 @@
 # 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 .basic import *
 from .generators import *

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

@@ -36,17 +36,12 @@ def eigenvector_centrality(
     G, max_iter=100, tol=1.0e-6, nstart=None, weight=None, *, dtype=None
 ):
     """`nstart` parameter is not used, but it is checked for validity."""
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if len(G) == 0:
         raise nx.NetworkXPointlessConcept(
             "cannot compute centrality for the null graph"
         )
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         # Check if given nstart is valid even though we don't use it
         nstart = G._dict_to_nodearray(nstart, dtype=dtype)

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

@@ -49,15 +49,10 @@ def katz_centrality(
         # Redundant with the `_can_run` check below when being dispatched by NetworkX,
         # but we raise here in case this funcion is called directly.
         raise NotImplementedError("normalized=False is not supported.")
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if (N := len(G)) == 0:
         return {}
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         # Check if given nstart is valid even though we don't use it
         nstart = G._dict_to_nodearray(nstart, 0, dtype)

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

@@ -46,15 +46,10 @@ def hits(
     weight="weight",
     dtype=None,
 ):
-    G = _to_graph(G, weight, np.float32)
+    G = _to_graph(G, weight, 1, np.float32)
     if (N := len(G)) == 0:
         return {}, {}
-    if dtype is not None:
-        dtype = _get_float_dtype(dtype)
-    elif weight in G.edge_values:
-        dtype = _get_float_dtype(G.edge_values[weight].dtype)
-    else:
-        dtype = np.float32
+    dtype = _get_float_dtype(dtype, graph=G, weight=weight)
     if nstart is not None:
         nstart = G._dict_to_nodearray(nstart, 0, dtype)
     if max_iter <= 0: