networkx: All return types from python-type-stubs (#14256)

Author: Avasam

stubs/networkx/networkx/algorithms/approximation/clique.pyi

@@ -1,12 +1,14 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["clique_removal", "max_clique", "large_clique_size", "maximum_independent_set"]
 
 @_dispatchable
-def maximum_independent_set(G: Graph[_Node]): ...
+def maximum_independent_set(G: Graph[_Node]) -> set[Incomplete]: ...
 @_dispatchable
-def max_clique(G: Graph[_Node]): ...
+def max_clique(G: Graph[_Node]) -> set[Incomplete]: ...
 @_dispatchable
 def clique_removal(G: Graph[_Node]): ...
 @_dispatchable

stubs/networkx/networkx/algorithms/approximation/clustering_coefficient.pyi

@@ -5,4 +5,4 @@ from numpy.random import RandomState
 __all__ = ["average_clustering"]
 
 @_dispatchable
-def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...
+def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None) -> float: ...

stubs/networkx/networkx/algorithms/approximation/connectivity.pyi

@@ -11,4 +11,6 @@ def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutof
 @_dispatchable
 def node_connectivity(G: Graph[_Node], s: _Node | None = None, t: _Node | None = None): ...
 @_dispatchable
-def all_pairs_node_connectivity(G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None): ...
+def all_pairs_node_connectivity(
+    G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, cutoff: int | None = None
+) -> dict[Incomplete, dict[Incomplete, Incomplete]]: ...

stubs/networkx/networkx/algorithms/approximation/dominating_set.pyi

@@ -1,9 +1,11 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"]
 
 @_dispatchable
-def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None): ...
+def min_weighted_dominating_set(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...
 @_dispatchable
-def min_edge_dominating_set(G: Graph[_Node]): ...
+def min_edge_dominating_set(G: Graph[_Node]) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/approximation/kcomponents.pyi

@@ -1,7 +1,10 @@
+from _typeshed import Incomplete
+from collections import defaultdict
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["k_components"]
 
 @_dispatchable
-def k_components(G: Graph[_Node], min_density: float = 0.95): ...
+def k_components(G: Graph[_Node], min_density: float = 0.95) -> defaultdict[Incomplete, list[Incomplete]]: ...

stubs/networkx/networkx/algorithms/approximation/matching.pyi

@@ -1,7 +1,9 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_maximal_matching"]
 
 @_dispatchable
-def min_maximal_matching(G: Graph[_Node]): ...
+def min_maximal_matching(G: Graph[_Node]) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/approximation/traveling_salesman.pyi

@@ -21,7 +21,7 @@ _SupportsLenAndGetItemT = TypeVar("_SupportsLenAndGetItemT", bound=SupportsLenAn
 def swap_two_nodes(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
 def move_one_node(soln: _SupportsLenAndGetItemT, seed) -> _SupportsLenAndGetItemT: ...
 @_dispatchable
-def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None): ...
+def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None) -> list[Incomplete]: ...
 @_dispatchable
 def traveling_salesman_problem(
     G: Graph[_Node],

stubs/networkx/networkx/algorithms/approximation/vertex_cover.pyi

@@ -1,7 +1,9 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["min_weighted_vertex_cover"]
 
 @_dispatchable
-def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None): ...
+def min_weighted_vertex_cover(G: Graph[_Node], weight: str | None = None) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/assortativity/connectivity.pyi

@@ -9,4 +9,4 @@ __all__ = ["average_degree_connectivity"]
 @_dispatchable
 def average_degree_connectivity(
     G: Graph[_Node], source="in+out", target="in+out", nodes: Iterable[Incomplete] | None = None, weight: str | None = None
-): ...
+) -> dict[Incomplete, int | float]: ...

stubs/networkx/networkx/algorithms/assortativity/correlation.pyi

@@ -14,12 +14,12 @@ __all__ = [
 @_dispatchable
 def degree_assortativity_coefficient(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
-): ...
+) -> float: ...
 @_dispatchable
 def degree_pearson_correlation_coefficient(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
-): ...
+) -> float: ...
 @_dispatchable
-def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
+def attribute_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...
 @_dispatchable
-def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None): ...
+def numeric_assortativity_coefficient(G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None) -> float: ...