networkx: All return types from python-type-stubs

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: ...

stubs/networkx/networkx/algorithms/assortativity/mixing.pyi

@@ -9,7 +9,7 @@ __all__ = ["attribute_mixing_matrix", "attribute_mixing_dict", "degree_mixing_ma
 @_dispatchable
 def attribute_mixing_dict(
     G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None, normalized: bool = False
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def attribute_mixing_matrix(
     G: Graph[_Node],
@@ -21,7 +21,7 @@ def attribute_mixing_matrix(
 @_dispatchable
 def degree_mixing_dict(
     G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes=None, normalized: bool = False
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def degree_mixing_matrix(
     G: Graph[_Node],
@@ -33,4 +33,4 @@ def degree_mixing_matrix(
     mapping: SupportsGetItem[Incomplete, Incomplete] | None = None,
 ): ...
 @_dispatchable
-def mixing_dict(xy, normalized: bool = False): ...
+def mixing_dict(xy, normalized: bool = False) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/assortativity/neighbor_degree.pyi

@@ -13,4 +13,4 @@ def average_neighbor_degree(
     target: str | None = "out",
     nodes: Iterable[Incomplete] | None = None,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/asteroidal.pyi

@@ -6,7 +6,7 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["is_at_free", "find_asteroidal_triple"]
 
 @_dispatchable
-def find_asteroidal_triple(G: Graph[_Node]): ...
+def find_asteroidal_triple(G: Graph[_Node]) -> list[Incomplete] | None: ...
 @_dispatchable
 def is_at_free(G: Graph[_Node]) -> bool: ...
 @_dispatchable

stubs/networkx/networkx/algorithms/bipartite/basic.pyi

@@ -7,14 +7,14 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["is_bipartite", "is_bipartite_node_set", "color", "sets", "density", "degrees"]
 
 @_dispatchable
-def color(G: Graph[_Node]): ...
+def color(G: Graph[_Node]) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def is_bipartite(G: Graph[_Node]) -> bool: ...
 @_dispatchable
 def is_bipartite_node_set(G: Graph[_Node], nodes: Iterable[Incomplete]) -> bool: ...
 @_dispatchable
-def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
+def sets(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> tuple[set[Incomplete], set[Incomplete]]: ...
 @_dispatchable
-def density(B: Graph[_Node], nodes): ...
+def density(B: Graph[_Node], nodes) -> float: ...
 @_dispatchable
-def degrees(B: Graph[_Node], nodes, weight: str | None = None): ...
+def degrees(B: Graph[_Node], nodes, weight: str | None = None) -> tuple[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/centrality.pyi

@@ -1,11 +1,13 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["degree_centrality", "betweenness_centrality", "closeness_centrality"]
 
 @_dispatchable
-def degree_centrality(G: Graph[_Node], nodes): ...
+def degree_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def betweenness_centrality(G: Graph[_Node], nodes): ...
+def betweenness_centrality(G: Graph[_Node], nodes) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True): ...
+def closeness_centrality(G: Graph[_Node], nodes, normalized: bool | None = True) -> dict[Incomplete, Incomplete]: ...

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

@@ -13,11 +13,13 @@ def cc_min(nu, nv) -> float: ...
 modes: dict[str, Callable[[Incomplete, Incomplete], float]]
 
 @_dispatchable
-def latapy_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
+def latapy_clustering(
+    G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"
+) -> dict[Incomplete, Incomplete]: ...
 
 clustering = latapy_clustering
 
 @_dispatchable
-def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...
+def average_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot") -> float: ...
 @_dispatchable
-def robins_alexander_clustering(G: Graph[_Node]): ...
+def robins_alexander_clustering(G: Graph[_Node]) -> float: ...

stubs/networkx/networkx/algorithms/bipartite/covering.pyi

@@ -7,4 +7,4 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["min_edge_cover"]
 
 @_dispatchable
-def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None): ...
+def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None) -> set[Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/edgelist.pyi

@@ -1,4 +1,3 @@
-from _typeshed import Incomplete
 from collections.abc import Generator
 
 from networkx.classes.graph import Graph, _Node
@@ -9,7 +8,7 @@ __all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgeli
 @_dispatchable
 def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
 @_dispatchable
-def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[Incomplete, None, None]: ...
+def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[str]: ...
 @_dispatchable
 def parse_edgelist(
     lines,

stubs/networkx/networkx/algorithms/bipartite/matching.pyi

@@ -7,9 +7,9 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["maximum_matching", "hopcroft_karp_matching", "eppstein_matching", "to_vertex_cover", "minimum_weight_full_matching"]
 
 @_dispatchable
-def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None): ...
+def hopcroft_karp_matching(G: Graph[_Node], top_nodes: Iterable[_Node] | None = None) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None): ...
+def eppstein_matching(G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def to_vertex_cover(
     G: Graph[_Node], matching: SupportsGetItem[Incomplete, Incomplete], top_nodes: Iterable[Incomplete] | None = None
@@ -20,4 +20,4 @@ maximum_matching = hopcroft_karp_matching
 @_dispatchable
 def minimum_weight_full_matching(
     G: Graph[_Node], top_nodes: Iterable[Incomplete] | None = None, weight: str | None = "weight"
-): ...
+) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/bipartite/matrix.pyi

@@ -14,6 +14,6 @@ def biadjacency_matrix(
     dtype=None,
     weight: str | None = "weight",
     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"): ...

stubs/networkx/networkx/algorithms/bipartite/redundancy.pyi

@@ -7,4 +7,4 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["node_redundancy"]
 
 @_dispatchable
-def node_redundancy(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None): ...
+def node_redundancy(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None) -> dict[Incomplete, float]: ...

stubs/networkx/networkx/algorithms/bipartite/spectral.pyi

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

stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness.pyi

@@ -1,3 +1,5 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState
@@ -18,12 +20,12 @@ def approximate_current_flow_betweenness_centrality(
     epsilon: float = 0.5,
     kmax: int = 10000,
     seed: int | RandomState | None = None,
-): ...
+) -> dict[Incomplete, float]: ...
 @_dispatchable
 def current_flow_betweenness_centrality(
     G: Graph[_Node], normalized: bool | None = True, weight: str | None = None, dtype: type = ..., solver: str = "full"
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def edge_current_flow_betweenness_centrality(
     G: Graph[_Node], normalized: bool | None = True, weight: str | None = None, dtype: type = ..., solver: str = "full"
-): ...
+) -> dict[tuple[Incomplete, Incomplete], float]: ...

stubs/networkx/networkx/algorithms/centrality/current_flow_betweenness_subset.pyi

@@ -1,3 +1,4 @@
+from _typeshed import Incomplete
 from collections.abc import Iterable
 
 from networkx.classes.graph import Graph, _Node
@@ -14,7 +15,7 @@ def current_flow_betweenness_centrality_subset(
     weight: str | None = None,
     dtype: type = ...,
     solver: str = "lu",
-): ...
+) -> dict[Incomplete, float]: ...
 @_dispatchable
 def edge_current_flow_betweenness_centrality_subset(
     G: Graph[_Node],
@@ -24,4 +25,4 @@ def edge_current_flow_betweenness_centrality_subset(
     weight: str | None = None,
     dtype: type = ...,
     solver: str = "lu",
-): ...
+) -> dict[tuple[Incomplete, Incomplete], float]: ...

stubs/networkx/networkx/algorithms/centrality/current_flow_closeness.pyi

@@ -1,9 +1,13 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["current_flow_closeness_centrality", "information_centrality"]
 
 @_dispatchable
-def current_flow_closeness_centrality(G: Graph[_Node], weight: str | None = None, dtype: type = ..., solver: str = "lu"): ...
+def current_flow_closeness_centrality(
+    G: Graph[_Node], weight: str | None = None, dtype: type = ..., solver: str = "lu"
+) -> dict[Incomplete, float]: ...
 
 information_centrality = current_flow_closeness_centrality

stubs/networkx/networkx/algorithms/centrality/eigenvector.pyi

@@ -12,8 +12,8 @@ def eigenvector_centrality(
     tol: float | None = 1e-06,
     nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, float]: ...
 @_dispatchable
 def eigenvector_centrality_numpy(
     G: Graph[_Node], weight: str | None = None, max_iter: int | None = 50, tol: float | None = 0
-): ...
+) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/centrality/group.pyi

@@ -26,12 +26,12 @@ def prominent_group(
     endpoints: bool | None = False,
     normalized: bool | None = True,
     greedy: bool | None = False,
-): ...
+) -> tuple[float, list[Incomplete]]: ...
 @_dispatchable
-def group_closeness_centrality(G: Graph[_Node], S: Iterable[Incomplete], weight: str | None = None): ...
+def group_closeness_centrality(G: Graph[_Node], S: Iterable[Incomplete], weight: str | None = None) -> float: ...
 @_dispatchable
-def group_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
+def group_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...
 @_dispatchable
-def group_in_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
+def group_in_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...
 @_dispatchable
-def group_out_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]): ...
+def group_out_degree_centrality(G: Graph[_Node], S: Iterable[Incomplete]) -> float: ...

stubs/networkx/networkx/algorithms/centrality/harmonic.pyi

@@ -9,4 +9,4 @@ __all__ = ["harmonic_centrality"]
 @_dispatchable
 def harmonic_centrality(
     G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, distance=None, sources: Iterable[Incomplete] | None = None
-): ...
+) -> dict[Incomplete, int]: ...

stubs/networkx/networkx/algorithms/centrality/katz.pyi

@@ -15,12 +15,12 @@ def katz_centrality(
     nstart: SupportsGetItem[Incomplete, Incomplete] | None = None,
     normalized: bool | None = True,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, Incomplete]: ...
 @_dispatchable
 def katz_centrality_numpy(
     G: Graph[_Node],
     alpha: float = 0.1,
     beta: float | SupportsGetItem[Incomplete, Incomplete] | None = 1.0,
     normalized: bool = True,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, Incomplete]: ...

stubs/networkx/networkx/algorithms/centrality/load.pyi

@@ -1,3 +1,5 @@
+from _typeshed import Incomplete
+
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
@@ -6,7 +8,7 @@ __all__ = ["load_centrality", "edge_load_centrality"]
 @_dispatchable
 def newman_betweenness_centrality(
     G: Graph[_Node], v=None, cutoff: bool | None = None, normalized: bool | None = True, weight: str | None = None
-): ...
+) -> float | dict[Incomplete, float]: ...
 
 load_centrality = newman_betweenness_centrality
 

stubs/networkx/networkx/algorithms/centrality/percolation.pyi

@@ -11,4 +11,4 @@ def percolation_centrality(
     attribute: str | None = "percolation",
     states: SupportsGetItem[Incomplete, Incomplete] | None = None,
     weight: str | None = None,
-): ...
+) -> dict[Incomplete, float]: ...