[stubsabot] Bump networkx to 3.7

stubs/networkx/METADATA.toml

@@ -1,6 +1,6 @@
-version = "3.6.1"
-upstream-repository = "https://github.com/networkx/networkx"
 # Requires a version of NumPy with `numpy.typing.NDArray`.
+version = "3.7"
+upstream-repository = "https://github.com/networkx/networkx"
 optional-dependencies = ["numpy>=1.21"]
 
 [tool.stubtest]

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

@@ -1,6 +1,7 @@
 from networkx.algorithms.bipartite.basic import *
 from networkx.algorithms.bipartite.centrality import *
 from networkx.algorithms.bipartite.cluster import *
+from networkx.algorithms.bipartite.community import *
 from networkx.algorithms.bipartite.covering import *
 from networkx.algorithms.bipartite.edgelist import *
 from networkx.algorithms.bipartite.extendability import *

stubs/networkx/networkx/algorithms/bipartite/cluster.pyi

@@ -4,7 +4,7 @@ from collections.abc import Callable, Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
-__all__ = ["clustering", "average_clustering", "latapy_clustering", "robins_alexander_clustering"]
+__all__ = ["butterflies", "clustering", "average_clustering", "latapy_clustering", "robins_alexander_clustering"]
 
 def cc_dot(nu, nv) -> float: ...
 def cc_max(nu, nv) -> float: ...
@@ -23,3 +23,5 @@ clustering = latapy_clustering
 def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot") -> float: ...
 @_dispatchable
 def robins_alexander_clustering(G: Graph[_Node]) -> float: ...
+@_dispatchable
+def butterflies(G: Graph[_Node], nodes: Iterable[_Node] | None = None) -> dict[_Node, int]: ...

stubs/networkx/networkx/algorithms/bipartite/community.pyi

@@ -0,0 +1,16 @@
+from collections.abc import Iterable, Set as AbstractSet
+
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+
+__all__ = ["modularity"]
+
+@_dispatchable
+def modularity(
+    G: Graph[_Node],
+    communities: Iterable[AbstractSet[_Node]],
+    nodes: Iterable[_Node],
+    *,
+    weight: str | None = "weight",
+    resolution: float = 1,
+) -> float: ...

stubs/networkx/networkx/algorithms/centrality/closeness.pyi

@@ -1,4 +1,5 @@
 from _typeshed import Incomplete, SupportsKeysAndGetItem
+from collections.abc import Mapping
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -7,7 +8,12 @@ __all__ = ["closeness_centrality", "incremental_closeness_centrality"]
 
 @_dispatchable
 def closeness_centrality(
-    G: Graph[_Node], u: _Node | None = None, distance=None, wf_improved: bool | None = True
+    G: Graph[_Node],
+    u: _Node | None = None,
+    distance=None,
+    wf_improved: bool | None = True,
+    *,
+    sp: Mapping[_Node, Mapping[_Node, float]] | None = None,
 ) -> dict[_Node, float]: ...
 @_dispatchable
 def incremental_closeness_centrality(

stubs/networkx/networkx/algorithms/community/community_utils.pyi

@@ -3,7 +3,9 @@ from collections.abc import Container, Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
-__all__ = ["is_partition"]
+__all__ = ["is_cover", "is_partition"]
 
 @_dispatchable
 def is_partition(G: Graph[_Node], communities: Iterable[Container[_Node]]) -> bool: ...
+@_dispatchable
+def is_cover(G: Graph[_Node], communities: Iterable[Container[_Node]]) -> bool: ...

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

@@ -1,20 +1,31 @@
-from _typeshed import Incomplete
+from collections.abc import Generator
+from random import Random
+from typing import Literal
 
+from networkx._typing import Seed
 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,
+    resolution: float = 1.0,
     max_level: int | None = None,
-    seed: int | RandomState | None = None,
-) -> list[Incomplete]: ...
+    seed: Seed | Random | None = None,
+    metric: Literal["cpm", "modularity"] = "cpm",
+    theta: float = 0.01,
+) -> list[set[_Node]]: ...
 @_dispatchable
 def leiden_partitions(
-    G: Graph[_Node], weight: str | None = "weight", resolution: float = 1, seed: int | RandomState | None = None
-): ...
+    G: Graph[_Node],
+    *,
+    weight: str | None = "weight",
+    metric: Literal["cpm", "modularity"] = "cpm",
+    resolution: float = 1.0,
+    seed: Seed | Random | None = None,
+    theta: float = 0.01,
+) -> Generator[list[set[_Node]]]: ...

stubs/networkx/networkx/algorithms/community/quality.pyi

@@ -1,12 +1,12 @@
 from _typeshed import Incomplete
-from collections.abc import Iterable
+from collections.abc import Iterable, Set as AbstractSet
 
 from networkx.classes.graph import Graph, _Node
 from networkx.exception import NetworkXError
 from networkx.utils.backends import _dispatchable
 from networkx.utils.decorators import argmap
 
-__all__ = ["modularity", "partition_quality"]
+__all__ = ["constant_potts_model", "overlapping_modularity", "modularity", "partition_quality"]
 
 class NotAPartition(NetworkXError):
     def __init__(self, G: Graph[_Node], collection) -> None: ...
@@ -25,3 +25,15 @@ def modularity(
 ) -> float: ...
 @_dispatchable
 def partition_quality(G: Graph[_Node], partition: Iterable[Incomplete]) -> tuple[float, float]: ...
+@_dispatchable
+def constant_potts_model(
+    G: Graph[_Node],
+    communities: Iterable[AbstractSet[_Node]],
+    weight: str | None = "weight",
+    node_weight: str | None = "node_weight",
+    resolution: float = 1,
+) -> float: ...
+@_dispatchable
+def overlapping_modularity(
+    G: Graph[_Node], communities: Iterable[AbstractSet[_Node]], *, weight: str | None = "weight", resolution: float = 1
+) -> float: ...

stubs/networkx/networkx/algorithms/dag.pyi

@@ -62,3 +62,5 @@ def dag_longest_path(
 def dag_longest_path_length(G: DiGraph[_Node], weight: str | None = "weight", default_weight: int | None = 1) -> int: ...
 @_dispatchable
 def dag_to_branching(G: DiGraph[_Node, _NodeData, _EdgeData]) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
+@_dispatchable
+def antichain_width(G: DiGraph[_Node]) -> int: ...

stubs/networkx/networkx/algorithms/distance_measures.pyi

@@ -7,6 +7,7 @@ from networkx.utils.backends import _dispatchable
 _WeightFunction: TypeAlias = Callable[..., int]
 
 __all__ = [
+    "centroid",
     "eccentricity",
     "diameter",
     "harmonic_diameter",
@@ -51,7 +52,7 @@ def barycenter(
     G: Graph[_Node],
     weight: str | _WeightFunction | None = None,
     attr: str | None = None,
-    sp: Mapping[_Node, Mapping[_Node, int]] | None = None,
+    sp: Mapping[_Node, Mapping[_Node, float]] | None = None,
 ) -> list[_Node]: ...
 @_dispatchable
 def resistance_distance(
@@ -61,3 +62,10 @@ def resistance_distance(
 def effective_graph_resistance(G: Graph[_Node], weight: str | None = None, invert_weight: bool = True) -> float: ...
 @_dispatchable
 def kemeny_constant(G: Graph[_Node], *, weight: str | None = None) -> float: ...
+@_dispatchable
+def centroid(
+    G: Graph[_Node],
+    weight: str | _WeightFunction | None = None,
+    attr: str | None = None,
+    sp: Mapping[_Node, Mapping[_Node, float]] | None = None,
+) -> list[_Node]: ...

stubs/networkx/networkx/algorithms/flow/boykovkolmogorov.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -11,7 +12,7 @@ def boykov_kolmogorov(
     G: Graph[_Node],
     s: _Node,
     t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     residual: Graph[_Node] | None = None,
     value_only: bool = False,
     cutoff: float | None = None,

stubs/networkx/networkx/algorithms/flow/dinitz_alg.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -11,7 +12,7 @@ def dinitz(
     G: Graph[_Node],
     s: _Node,
     t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     residual: Graph[_Node] | None = None,
     value_only: bool = False,
     cutoff: float | None = None,

stubs/networkx/networkx/algorithms/flow/edmondskarp.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -11,7 +12,7 @@ def edmonds_karp(
     G: Graph[_Node],
     s: _Node,
     t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     residual: Graph[_Node] | None = None,
     value_only: bool = False,
     cutoff: float | None = None,

stubs/networkx/networkx/algorithms/flow/gomory_hu.pyi

@@ -1,6 +1,7 @@
 from _typeshed import Incomplete
 from collections.abc import Callable
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -11,5 +12,5 @@ default_flow_func = edmonds_karp
 
 @_dispatchable
 def gomory_hu_tree(
-    G: Graph[_Node], capacity: str = "capacity", flow_func: Callable[..., Incomplete] | None = None
+    G: Graph[_Node], capacity: str | _CapacityFunc[_Node] = "capacity", flow_func: Callable[..., Incomplete] | None = None
 ) -> Graph[Incomplete]: ...

stubs/networkx/networkx/algorithms/flow/maxflow.pyi

@@ -1,6 +1,7 @@
 from _typeshed import Incomplete
 from collections.abc import Callable
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -14,7 +15,7 @@ def maximum_flow(
     flowG: Graph[_Node],
     _s: _Node,
     _t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     flow_func: Callable[..., Incomplete] | None = None,
     **kwargs,
 ) -> tuple[int | float, dict[Incomplete, Incomplete]]: ...
@@ -23,7 +24,7 @@ def maximum_flow_value(
     flowG: Graph[_Node],
     _s: _Node,
     _t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     flow_func: Callable[..., Incomplete] | None = None,
     **kwargs,
 ) -> int | float: ...
@@ -32,7 +33,7 @@ def minimum_cut(
     flowG: Graph[_Node],
     _s: _Node,
     _t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     flow_func: Callable[..., Incomplete] | None = None,
     **kwargs,
 ) -> tuple[int | float, tuple[set[Incomplete], set[Incomplete]]]: ...
@@ -41,7 +42,7 @@ def minimum_cut_value(
     flowG: Graph[_Node],
     _s: _Node,
     _t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     flow_func: Callable[..., Incomplete] | None = None,
     **kwargs,
 ) -> int | float: ...

stubs/networkx/networkx/algorithms/flow/preflowpush.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -11,7 +12,7 @@ def preflow_push(
     G: Graph[_Node],
     s: _Node,
     t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     residual: Graph[_Node] | None = None,
     global_relabel_freq: float = 1,
     value_only: bool = False,

stubs/networkx/networkx/algorithms/flow/shortestaugmentingpath.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
 
+from networkx.algorithms.flow.utils import _CapacityFunc
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -11,7 +12,7 @@ def shortest_augmenting_path(
     G: Graph[_Node],
     s: _Node,
     t: _Node,
-    capacity: str = "capacity",
+    capacity: str | _CapacityFunc[_Node] = "capacity",
     residual: Graph[_Node] | None = None,
     value_only: bool = False,
     two_phase: bool = False,

stubs/networkx/networkx/algorithms/flow/utils.pyi

@@ -1,5 +1,6 @@
 from _typeshed import Incomplete
-from typing import overload
+from collections.abc import Callable
+from typing import Any, TypeAlias, overload
 from typing_extensions import Never, deprecated
 
 from networkx.classes.graph import Graph, _Node
@@ -29,11 +30,17 @@ class GlobalRelabelThreshold:
 
 @overload
 @deprecated("MultiGraph and MultiDiGraph not supported (yet).")
-def build_residual_network(G: MultiGraph[_Node], capacity, *, backend: str | None = None, **backend_kwargs) -> Never: ...
+def build_residual_network(
+    G: MultiGraph[_Node], capacity: str | _CapacityFunc[_Node], *, backend: str | None = None, **backend_kwargs
+) -> Never: ...
 @overload
-def build_residual_network(G: Graph[_Node], capacity, *, backend: str | None = None, **backend_kwargs): ...
+def build_residual_network(
+    G: Graph[_Node], capacity: str | _CapacityFunc[_Node], *, backend: str | None = None, **backend_kwargs
+): ...
 
 @_dispatchable
 def detect_unboundedness(R, s, t) -> None: ...
 @_dispatchable
 def build_flow_dict(G: Graph[_Node], R): ...
+
+_CapacityFunc: TypeAlias = Callable[[_Node, _Node, dict[str, Any]], float]

stubs/networkx/networkx/algorithms/isomorphism/ismags.pyi

@@ -30,3 +30,5 @@ class ISMAGS:
     def subgraph_is_isomorphic(self, symmetry: bool = False) -> bool: ...
     def isomorphisms_iter(self, symmetry: bool = True) -> Generator[Incomplete, Incomplete]: ...
     def subgraph_isomorphisms_iter(self, symmetry: bool = True): ...
+    def is_monomorphic(self, symmetry: bool = False) -> bool: ...
+    def monomorphisms_iter(self, symmetry: bool = True) -> Generator[dict[Hashable, Hashable]]: ...

stubs/networkx/networkx/algorithms/isomorphism/isomorph.pyi

@@ -1,6 +1,5 @@
 from _typeshed import Incomplete
 from collections.abc import Callable
-from typing_extensions import deprecated
 
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
@@ -9,22 +8,10 @@ __all__ = ["could_be_isomorphic", "fast_could_be_isomorphic", "faster_could_be_i
 
 @_dispatchable
 def could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node], *, properties: str = "dtc") -> bool: ...
-@deprecated("`graph_could_be_isomorphic` is a deprecated alias for `could_be_isomorphic`. Use `could_be_isomorphic` instead.")
-def graph_could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node]) -> bool: ...
 @_dispatchable
 def fast_could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node]) -> bool: ...
-@deprecated(
-    "`fast_graph_could_be_isomorphic` is a deprecated alias for `fast_could_be_isomorphic`. "
-    "Use `fast_could_be_isomorphic` instead."
-)
-def fast_graph_could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node]) -> bool: ...
 @_dispatchable
 def faster_could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node]) -> bool: ...
-@deprecated(
-    "`faster_graph_could_be_isomorphic` is a deprecated alias for `faster_could_be_isomorphic`. "
-    "Use `faster_could_be_isomorphic` instead."
-)
-def faster_graph_could_be_isomorphic(G1: Graph[_Node], G2: Graph[_Node]) -> bool: ...
 @_dispatchable
 def is_isomorphic(
     G1: Graph[_Node],