[networkx] Update to 3.6.1

stubs/networkx/METADATA.toml

@@ -1,4 +1,4 @@
-version = "3.6"
+version = "3.6.1"
 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/bipartite/matrix.pyi

@@ -16,4 +16,11 @@ def biadjacency_matrix(
     format="csr",
 ): ...  # Return is a complex union of scipy classes depending on the format param
 @_dispatchable
-def from_biadjacency_matrix(A, create_using: Graph[_Node] | None = None, edge_attribute: str = "weight"): ...
+def from_biadjacency_matrix(
+    A,
+    create_using: Graph[_Node] | None = None,
+    edge_attribute: str = "weight",
+    *,
+    row_order: Iterable[Incomplete] | None = None,
+    column_order: Iterable[Incomplete] | None = None,
+): ...

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

@@ -1,9 +1,9 @@
 from networkx.algorithms.community.asyn_fluid import *
+from networkx.algorithms.community.bipartitions import *
 from networkx.algorithms.community.centrality import *
 from networkx.algorithms.community.community_utils import *
 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 *

stubs/networkx/networkx/algorithms/community/bipartitions.pyi

@@ -0,0 +1,22 @@
+from _typeshed import Incomplete
+from collections.abc import Iterable
+
+from networkx.algorithms.shortest_paths.weighted import _WeightFunc
+from networkx.classes.graph import Graph, _Node
+from networkx.utils.backends import _dispatchable
+from numpy.random import RandomState
+
+__all__ = ["kernighan_lin_bisection", "spectral_modularity_bipartition", "greedy_node_swap_bipartition"]
+
+@_dispatchable
+def kernighan_lin_bisection(
+    G: Graph[_Node],
+    partition: tuple[Iterable[Incomplete], Iterable[Incomplete]] | None = None,
+    max_iter: int = 10,
+    weight: str | _WeightFunc[_Node] = "weight",
+    seed: int | RandomState | None = None,
+) -> tuple[set[Incomplete], set[Incomplete]]: ...
+def spectral_modularity_bipartition(G: Graph[_Node]) -> tuple[set[Incomplete], set[Incomplete]]: ...
+def greedy_node_swap_bipartition(
+    G: Graph[_Node], *, init_split: tuple[set[Incomplete], set[Incomplete]] | None = None, max_iter: int = 10
+) -> tuple[set[Incomplete], set[Incomplete]]: ...

stubs/networkx/networkx/classes/graph.pyi

@@ -43,7 +43,7 @@ class Graph(Collection[_Node]):
     def to_undirected_class(self) -> type[Graph[_Node]]: ...
     # @_dispatchable adds `backend` argument, but this decorated is unsupported constructor type here
     # and __init__() ignores this argument
-    def __new__(cls, incoming_graph_data: _Data[_Node] | None = None, *, backend=None, **attr: Any) -> Self: ...
+    def __new__(cls, *args, backend=None, **kwargs) -> Self: ...
     def __init__(self, incoming_graph_data: _Data[_Node] | None = None, **attr: Any) -> None: ...  # attr: key=value pairs
     @cached_property
     def adj(self) -> AdjacencyView[_Node, _Node, dict[str, Any]]: ...

stubs/networkx/networkx/classes/multigraph.pyi

@@ -24,7 +24,7 @@ class MultiGraph(Graph[_Node]):
     def to_undirected_class(self) -> type[MultiGraph[_Node]]: ...
     # @_dispatchable adds `backend` argument, but this decorated is unsupported constructor type here
     # and __init__() ignores this argument
-    def __new__(cls, incoming_graph_data=None, multigraph_input: bool | None = None, *, backend=None, **attr: Any) -> Self: ...
+    def __new__(cls, *args, backend=None, **kwargs) -> Self: ...
     def __init__(self, incoming_graph_data=None, multigraph_input: bool | None = None, **attr: Any) -> None: ...
     @cached_property
     def adj(self) -> MultiAdjacencyView[_Node, _Node, dict[str, Any]]: ...  # data can be any type