networkx: Add type annotation to all G params (#14796)

Author: Avasam

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

@@ -6,9 +6,11 @@ from networkx.utils.backends import _dispatchable
 __all__ = ["generate_edgelist", "write_edgelist", "parse_edgelist", "read_edgelist"]
 
 @_dispatchable
-def write_edgelist(G, path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8") -> None: ...
+def write_edgelist(
+    G: Graph[_Node], path, comments: str = "#", delimiter: str = " ", data: bool = True, encoding: str = "utf-8"
+) -> None: ...
 @_dispatchable
-def generate_edgelist(G, delimiter: str = " ", data: bool = True) -> Generator[str]: ...
+def generate_edgelist(G: Graph[_Node], delimiter: str = " ", data: bool = True) -> Generator[str]: ...
 @_dispatchable
 def parse_edgelist(
     lines,

stubs/networkx/networkx/algorithms/centrality/flow_matrix.pyi

@@ -1,10 +1,11 @@
 from _typeshed import Incomplete
 from collections.abc import Generator
 
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 @_dispatchable
-def flow_matrix_row(G, weight=None, dtype=..., solver: str = "lu") -> Generator[Incomplete, None, None]: ...
+def flow_matrix_row(G: Graph[_Node], weight=None, dtype=..., solver: str = "lu") -> Generator[Incomplete, None, None]: ...
 
 class InverseLaplacian:
     dtype: Incomplete

stubs/networkx/networkx/algorithms/chordal.pyi

@@ -26,4 +26,4 @@ def chordal_graph_cliques(G: Graph[_Node]) -> Generator[frozenset[_Node], None,
 @_dispatchable
 def chordal_graph_treewidth(G: Graph[_Node]) -> int: ...
 @_dispatchable
-def complete_to_chordal_graph(G) -> tuple[Incomplete, dict[Incomplete, int]]: ...
+def complete_to_chordal_graph(G: Graph[_Node]) -> tuple[Incomplete, dict[Incomplete, int]]: ...

stubs/networkx/networkx/algorithms/clique.pyi

@@ -34,9 +34,9 @@ def node_clique_number(
 ) -> dict[_Node, int]: ...
 @overload
 def node_clique_number(G: Graph[_Node], nodes=None, cliques: Iterable[Incomplete] | None = None, separate_nodes=False) -> int: ...
-def number_of_cliques(G, nodes=None, cliques=None) -> int | dict[Incomplete, Incomplete]: ...
+def number_of_cliques(G: Graph[_Node], nodes=None, cliques=None) -> int | dict[Incomplete, Incomplete]: ...
 @_dispatchable
-def max_weight_clique(G, weight="weight") -> tuple[list[Incomplete], int]: ...
+def max_weight_clique(G: Graph[_Node], weight="weight") -> tuple[list[Incomplete], int]: ...
 
 class MaxWeightClique:
     G: Graph[Incomplete]

stubs/networkx/networkx/algorithms/coloring/greedy_coloring.pyi

@@ -1,4 +1,4 @@
-from _typeshed import Incomplete
+from _typeshed import Incomplete, Unused
 from collections.abc import Callable, Generator
 from typing import Final
 
@@ -18,21 +18,23 @@ __all__ = [
 ]
 
 @_dispatchable
-def strategy_largest_first(G, colors): ...
+def strategy_largest_first(G: Graph[_Node], colors: Unused): ...
 @_dispatchable
-def strategy_random_sequential(G, colors, seed=None): ...
+def strategy_random_sequential(G: Graph[_Node], colors: Unused, seed=None): ...
 @_dispatchable
-def strategy_smallest_last(G, colors): ...
+def strategy_smallest_last(G: Graph[_Node], colors: Unused): ...
 @_dispatchable
-def strategy_independent_set(G, colors) -> Generator[Incomplete, Incomplete, None]: ...
+def strategy_independent_set(G: Graph[_Node], colors: Unused) -> Generator[Incomplete, Incomplete, None]: ...
 @_dispatchable
-def strategy_connected_sequential_bfs(G, colors): ...
+def strategy_connected_sequential_bfs(G: Graph[_Node], colors): ...
 @_dispatchable
-def strategy_connected_sequential_dfs(G, colors): ...
+def strategy_connected_sequential_dfs(G: Graph[_Node], colors): ...
 @_dispatchable
-def strategy_connected_sequential(G, colors, traversal: str = "bfs") -> Generator[Incomplete, None, None]: ...
+def strategy_connected_sequential(
+    G: Graph[_Node], colors: Unused, traversal: str = "bfs"
+) -> Generator[Incomplete, None, None]: ...
 @_dispatchable
-def strategy_saturation_largest_first(G, colors) -> Generator[Incomplete, None, Incomplete]: ...
+def strategy_saturation_largest_first(G: Graph[_Node], colors) -> Generator[Incomplete, None, Incomplete]: ...
 
 STRATEGIES: Final[dict[str, Callable[..., Incomplete]]]
 

stubs/networkx/networkx/algorithms/community/label_propagation.pyi

@@ -9,7 +9,7 @@ from numpy.random import RandomState
 __all__ = ["label_propagation_communities", "asyn_lpa_communities", "fast_label_propagation_communities"]
 
 @_dispatchable
-def fast_label_propagation_communities(G, *, weight=None, seed=None) -> Generator[Incomplete]: ...
+def fast_label_propagation_communities(G: Graph[_Node], *, weight=None, seed=None) -> Generator[Incomplete]: ...
 @_dispatchable
 def asyn_lpa_communities(
     G: Graph[_Node], weight: str | None = None, seed: int | RandomState | None = None

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

@@ -6,7 +6,7 @@ from networkx.utils.decorators import argmap
 __all__ = ["modularity", "partition_quality"]
 
 class NotAPartition(NetworkXError):
-    def __init__(self, G, collection) -> None: ...
+    def __init__(self, G: Graph[_Node], collection) -> None: ...
 
 require_partition: argmap
 

stubs/networkx/networkx/algorithms/connectivity/edge_kcomponents.pyi

@@ -18,7 +18,7 @@ class EdgeComponentAuxGraph:
     H: Incomplete
 
     @classmethod
-    def construct(cls, G): ...
+    def construct(cls, G: Graph[_Node]): ...
     def k_edge_components(self, k: int) -> Generator[Incomplete, Incomplete, None]: ...
     def k_edge_subgraphs(self, k: int) -> Generator[Incomplete, Incomplete, None]: ...
 

stubs/networkx/networkx/algorithms/connectivity/utils.pyi

@@ -1,8 +1,9 @@
+from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["build_auxiliary_node_connectivity", "build_auxiliary_edge_connectivity"]
 
 @_dispatchable
-def build_auxiliary_node_connectivity(G): ...
+def build_auxiliary_node_connectivity(G: Graph[_Node]): ...
 @_dispatchable
-def build_auxiliary_edge_connectivity(G): ...
+def build_auxiliary_edge_connectivity(G: Graph[_Node]): ...

stubs/networkx/networkx/algorithms/d_separation.pyi

@@ -9,7 +9,7 @@ __all__ = ["is_d_separator", "is_minimal_d_separator", "find_minimal_d_separator
 @_dispatchable
 def is_d_separator(G: DiGraph[_Node], x: _Node | set[_Node], y: _Node | set[_Node], z: _Node | set[_Node]) -> bool: ...
 @_dispatchable
-def find_minimal_d_separator(G, x, y, *, included=None, restricted=None) -> set[Incomplete] | None: ...
+def find_minimal_d_separator(G: DiGraph[_Node], x, y, *, included=None, restricted=None) -> set[Incomplete] | None: ...
 @_dispatchable
 def is_minimal_d_separator(
     G: DiGraph[_Node],