Bump networkx to 3.5 (python#14196)

Author: donBarbos

stubs/networkx/@tests/stubtest_allowlist.txt

@@ -16,9 +16,6 @@ networkx\.(algorithms\.)?(centrality\.)?(current_flow_closeness\.)?information_c
 networkx\.(generators\.)?(random_graphs\.)?binomial_graph
 networkx\.(generators\.)?(random_graphs\.)?erdos_renyi_graph
 networkx\.(algorithms\.(minors\.(contraction\.)?)?)?identified_nodes
-networkx\.algorithms\.isomorphism\.isomorph\.faster_graph_could_be_isomorphic
-networkx\.algorithms\.isomorphism\.isomorph\.fast_graph_could_be_isomorphic
-networkx\.algorithms\.isomorphism\.isomorph\.graph_could_be_isomorphic
 networkx\.algorithms\.flow\.maxflow\.default_flow_func
 networkx\.algorithms\.[a-z_\.]+\.default_flow_func
 networkx\.(algorithms\.(centrality\.(load\.)?)?)?load_centrality

stubs/networkx/METADATA.toml

@@ -1,4 +1,4 @@
-version = "3.4.2"
+version = "3.5"
 upstream_repository = "https://github.com/networkx/networkx"
 # requires a version of numpy with a `py.typed` file
 requires = ["numpy>=1.20"]

stubs/networkx/networkx/algorithms/approximation/__init__.pyi

@@ -1,6 +1,7 @@
 from networkx.algorithms.approximation.clique import *
 from networkx.algorithms.approximation.clustering_coefficient import *
 from networkx.algorithms.approximation.connectivity import *
+from networkx.algorithms.approximation.density import *
 from networkx.algorithms.approximation.distance_measures import *
 from networkx.algorithms.approximation.dominating_set import *
 from networkx.algorithms.approximation.kcomponents import *

stubs/networkx/networkx/algorithms/approximation/density.pyi

@@ -0,0 +1,15 @@
+from collections.abc import Callable, Hashable
+from typing import Literal
+from typing_extensions import TypeAlias
+
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+
+_Algorithm: TypeAlias = Literal["greedy++", "fista"]
+
+__all__ = ["densest_subgraph"]
+
+ALGORITHMS: dict[_Algorithm, Callable[[Graph[Hashable], int], tuple[float, set[int]]]]
+
+@_dispatchable
+def densest_subgraph(G: Graph[_Node], iterations: int = 1, *, method: _Algorithm = "fista") -> tuple[float, set[int]]: ...

stubs/networkx/networkx/algorithms/approximation/treewidth.pyi

@@ -16,6 +16,6 @@ class MinDegreeHeuristic:
     def __init__(self, graph) -> None: ...
     def best_node(self, graph): ...
 
-def min_fill_in_heuristic(graph) -> Incomplete | None: ...
+def min_fill_in_heuristic(graph_dict) -> Incomplete | None: ...
 @_dispatchable
 def treewidth_decomp(G: Graph[_Node], heuristic=...) -> tuple[int, Graph[_Node]]: ...

stubs/networkx/networkx/algorithms/bipartite/__init__.pyi

@@ -5,6 +5,7 @@ from networkx.algorithms.bipartite.covering import *
 from networkx.algorithms.bipartite.edgelist import *
 from networkx.algorithms.bipartite.extendability import *
 from networkx.algorithms.bipartite.generators import *
+from networkx.algorithms.bipartite.link_analysis import *
 from networkx.algorithms.bipartite.matching import *
 from networkx.algorithms.bipartite.matrix import *
 from networkx.algorithms.bipartite.projection import *

stubs/networkx/networkx/algorithms/bipartite/link_analysis.pyi

@@ -0,0 +1,20 @@
+from collections.abc import Iterable
+
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+
+__all__ = ["birank"]
+
+@_dispatchable
+def birank(
+    G: Graph[_Node],
+    nodes: Iterable[_Node],
+    *,
+    alpha: float | None = None,
+    beta: float | None = None,
+    top_personalization: dict[str, int] | None = None,
+    bottom_personalization: dict[str, int] | None = None,
+    max_iter: int = 100,
+    tol: float = 1.0e-6,
+    weight: str | None = "weight",
+) -> dict[_Node, float]: ...

stubs/networkx/networkx/algorithms/community/__init__.pyi

@@ -5,6 +5,8 @@ from networkx.algorithms.community.divisive import *
 from networkx.algorithms.community.kclique import *
 from networkx.algorithms.community.kernighan_lin import *
 from networkx.algorithms.community.label_propagation import *
+from networkx.algorithms.community.leiden import *
+from networkx.algorithms.community.local import *
 from networkx.algorithms.community.louvain import *
 from networkx.algorithms.community.lukes import *
 from networkx.algorithms.community.modularity_max import *

stubs/networkx/networkx/algorithms/community/leiden.pyi

@@ -0,0 +1,18 @@
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+from numpy.random import RandomState
+
+__all__ = ["leiden_communities", "leiden_partitions"]
+
+@_dispatchable
+def leiden_communities(
+    G: Graph[_Node],
+    weight: str | None = "weight",
+    resolution: float = 1,
+    max_level: int | None = None,
+    seed: int | RandomState | None = None,
+): ...
+@_dispatchable
+def leiden_partitions(
+    G: Graph[_Node], weight: str | None = "weight", resolution: float = 1, seed: int | RandomState | None = None
+): ...

stubs/networkx/networkx/algorithms/community/local.pyi

@@ -0,0 +1,15 @@
+from collections.abc import Callable, Hashable
+from typing import Literal
+from typing_extensions import TypeAlias
+
+from networkx.classes.graph import Graph, _Node
+
+__all__ = ["greedy_source_expansion"]
+
+_Algorithm: TypeAlias = Literal["clauset"]
+
+ALGORITHMS: dict[_Algorithm, Callable[[Graph[Hashable], Hashable, int | None], set[Hashable]]]
+
+def greedy_source_expansion(
+    G: Graph[_Node], *, source: _Node, cutoff: int | None = None, method: _Algorithm = "clauset"
+) -> set[_Node | None]: ...