[networkx] Add generic type for node and edge data

stubs/networkx/networkx/algorithms/clique.pyi

@@ -2,7 +2,7 @@ from _typeshed import Incomplete
 from collections.abc import Generator, Iterable, Iterator
 from typing import overload
 
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _EdgeData, _Node, _NodeData
 from networkx.utils.backends import _dispatchable
 
 __all__ = [
@@ -23,10 +23,15 @@ def find_cliques(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None) ->
 @_dispatchable
 def find_cliques_recursive(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None) -> Iterator[list[_Node]]: ...
 @_dispatchable
-def make_max_clique_graph(G: Graph[_Node], create_using: Graph[_Node] | None = None) -> Graph[_Node]: ...
+def make_max_clique_graph(
+    G: Graph[_Node], create_using: Graph[_Node, _NodeData, _EdgeData] | None = None
+) -> Graph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
 def make_clique_bipartite(
-    G: Graph[_Node], fpos: bool | None = None, create_using: Graph[_Node] | None = None, name=None
+    G: Graph[_Node, _NodeData, _EdgeData],
+    fpos: bool | None = None,
+    create_using: Graph[_Node, _NodeData, _EdgeData] | None = None,
+    name=None,
 ) -> Graph[_Node]: ...
 @overload
 def node_clique_number(

stubs/networkx/networkx/algorithms/dag.pyi

@@ -2,7 +2,7 @@ from _typeshed import Incomplete
 from collections.abc import Callable, Generator, Iterable
 
 from networkx.classes.digraph import DiGraph
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _EdgeData, _Node, _NodeData
 from networkx.utils.backends import _dispatchable
 
 __all__ = [
@@ -40,11 +40,13 @@ def all_topological_sorts(G: DiGraph[_Node]) -> Generator[list[_Node]]: ...
 @_dispatchable
 def is_aperiodic(G: DiGraph[_Node]) -> bool: ...
 @_dispatchable
-def transitive_closure(G: Graph[_Node], reflexive=False) -> Graph[_Node]: ...
+def transitive_closure(G: Graph[_Node, _NodeData, _EdgeData], reflexive=False) -> Graph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
-def transitive_closure_dag(G: DiGraph[_Node], topo_order: Iterable[Incomplete] | None = None) -> DiGraph[_Node]: ...
+def transitive_closure_dag(
+    G: DiGraph[_Node, _NodeData, _EdgeData], topo_order: Iterable[Incomplete] | None = None
+) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
-def transitive_reduction(G: DiGraph[_Node]) -> DiGraph[_Node]: ...
+def transitive_reduction(G: DiGraph[_Node, _NodeData, _EdgeData]) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
 def antichains(G: DiGraph[_Node], topo_order: Iterable[Incomplete] | None = None) -> Generator[list[_Node]]: ...
 @_dispatchable
@@ -57,4 +59,4 @@ def dag_longest_path(
 @_dispatchable
 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]) -> DiGraph[_Node]: ...
+def dag_to_branching(G: DiGraph[_Node, _NodeData, _EdgeData]) -> DiGraph[_Node, _NodeData, _EdgeData]: ...

stubs/networkx/networkx/algorithms/planarity.pyi

@@ -4,7 +4,7 @@ from decimal import Decimal
 from typing import NoReturn
 
 from networkx.classes.digraph import DiGraph
-from networkx.classes.graph import Graph, _EdgePlus, _Node
+from networkx.classes.graph import Graph, _EdgeData, _EdgePlus, _Node, _NodeData
 from networkx.utils.backends import _dispatchable
 
 __all__ = ["check_planarity", "is_planar", "PlanarEmbedding"]
@@ -14,9 +14,9 @@ def is_planar(G: Graph[_Node]) -> bool: ...
 @_dispatchable
 def check_planarity(G: Graph[_Node], counterexample: bool = False): ...
 @_dispatchable
-def get_counterexample(G: Graph[_Node]) -> Graph[_Node]: ...
+def get_counterexample(G: Graph[_Node, _NodeData, _EdgeData]) -> Graph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
-def get_counterexample_recursive(G: Graph[_Node]) -> Graph[_Node]: ...
+def get_counterexample_recursive(G: Graph[_Node, _NodeData, _EdgeData]) -> Graph[_Node, _NodeData, _EdgeData]: ...
 
 class Interval:
     low: Incomplete

stubs/networkx/networkx/algorithms/traversal/breadth_first_search.pyi

@@ -2,7 +2,7 @@ from collections.abc import Callable, Generator, Iterable, Iterator
 from typing import Final, Literal
 
 from networkx.classes.digraph import DiGraph
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _EdgeData, _Node, _NodeData
 from networkx.utils.backends import _dispatchable
 
 __all__ = [
@@ -30,12 +30,12 @@ def bfs_edges(
 ) -> Generator[tuple[_Node, _Node]]: ...
 @_dispatchable
 def bfs_tree(
-    G: Graph[_Node],
+    G: Graph[_Node, _NodeData, _EdgeData],
     source: _Node,
     reverse: bool | None = False,
     depth_limit: int | None = None,
     sort_neighbors: Callable[[Iterator[_Node]], Iterable[_Node]] | None = None,
-) -> DiGraph[_Node]: ...
+) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
 def bfs_predecessors(
     G: Graph[_Node],

stubs/networkx/networkx/algorithms/traversal/depth_first_search.pyi

@@ -2,7 +2,7 @@ from collections.abc import Callable, Generator, Iterable, Iterator
 from typing import Literal
 
 from networkx.classes.digraph import DiGraph
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _EdgeData, _Node, _NodeData
 from networkx.utils.backends import _dispatchable
 
 __all__ = [
@@ -25,12 +25,12 @@ def dfs_edges(
 ) -> Generator[tuple[_Node, _Node]]: ...
 @_dispatchable
 def dfs_tree(
-    G: Graph[_Node],
+    G: Graph[_Node, _NodeData, _EdgeData],
     source: _Node | None = None,
     depth_limit: int | None = None,
     *,
     sort_neighbors: Callable[[Iterator[_Node]], Iterable[_Node]] | None = None,
-) -> DiGraph[_Node]: ...
+) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
 @_dispatchable
 def dfs_predecessors(
     G: Graph[_Node],

stubs/networkx/networkx/classes/digraph.pyi

@@ -4,7 +4,7 @@ from typing import Any
 from typing_extensions import Self
 
 from networkx.classes.coreviews import AdjacencyView
-from networkx.classes.graph import Graph, _Node
+from networkx.classes.graph import Graph, _EdgeData, _Node, _NodeData
 from networkx.classes.reportviews import (
     DiDegreeView,
     InDegreeView,
@@ -21,7 +21,7 @@ __all__ = ["DiGraph"]
 # NOTE: Graph subclasses relationships are so complex
 # we're only overriding methods that differ in signature from the base classes
 # to use inheritance to our advantage and reduce complexity
-class DiGraph(Graph[_Node]):
+class DiGraph(Graph[_Node, _NodeData, _EdgeData]):
     @cached_property
     def succ(self) -> AdjacencyView[_Node, _Node, dict[str, Any]]: ...
     @cached_property
@@ -34,19 +34,19 @@ class DiGraph(Graph[_Node]):
 
     def predecessors(self, n: _Node) -> Iterator[_Node]: ...
     @cached_property
-    def edges(self) -> OutEdgeView[_Node]: ...
+    def edges(self) -> OutEdgeView[_Node, _NodeData, _EdgeData]: ...
     @cached_property
-    def out_edges(self) -> OutEdgeView[_Node]: ...
+    def out_edges(self) -> OutEdgeView[_Node, _NodeData, _EdgeData]: ...
     @cached_property
     # Including subtypes' possible return types for LSP
-    def in_edges(self) -> InEdgeView[_Node] | InMultiEdgeView[_Node]: ...
+    def in_edges(self) -> InEdgeView[_Node, _NodeData, _EdgeData] | InMultiEdgeView[_Node, _NodeData, _EdgeData]: ...
     @cached_property
-    def degree(self) -> DiDegreeView[_Node]: ...
+    def degree(self) -> DiDegreeView[_Node, _NodeData, _EdgeData]: ...
     @cached_property
     # Including subtypes' possible return types for LSP
-    def in_degree(self) -> InDegreeView[_Node] | InMultiDegreeView[_Node]: ...
+    def in_degree(self) -> InDegreeView[_Node, _NodeData, _EdgeData] | InMultiDegreeView[_Node, _NodeData, _EdgeData]: ...
     @cached_property
     # Including subtypes' possible return types for LSP
-    def out_degree(self) -> OutDegreeView[_Node] | OutMultiDegreeView[_Node]: ...
-    def to_undirected(self, reciprocal: bool = False, as_view: bool = False) -> Graph[_Node]: ...  # type: ignore[override] # Has an additional `reciprocal` keyword argument
+    def out_degree(self) -> OutDegreeView[_Node, _NodeData, _EdgeData] | OutMultiDegreeView[_Node, _NodeData, _EdgeData]: ...
+    def to_undirected(self, reciprocal: bool = False, as_view: bool = False) -> Graph[_Node, _NodeData, _EdgeData]: ...  # type: ignore[override] # Has an additional `reciprocal` keyword argument
     def reverse(self, copy: bool = True) -> Self: ...

stubs/networkx/networkx/classes/function.pyi

@@ -5,7 +5,7 @@ from typing import Literal, TypeVar, overload
 from networkx import _dispatchable
 from networkx.algorithms.planarity import PlanarEmbedding
 from networkx.classes.digraph import DiGraph
-from networkx.classes.graph import Graph, _NBunch, _Node
+from networkx.classes.graph import Graph, _EdgeData, _NBunch, _Node, _NodeData
 from networkx.classes.multigraph import MultiGraph
 
 __all__ = [
@@ -73,7 +73,7 @@ def add_star(G_to_add_to, nodes_for_star, **attr) -> None: ...
 def add_path(G_to_add_to, nodes_for_path, **attr) -> None: ...
 def add_cycle(G_to_add_to, nodes_for_cycle, **attr) -> None: ...
 def subgraph(G: Graph[_Node], nbunch): ...
-def induced_subgraph(G: Graph[_Node], nbunch: _NBunch[_Node]) -> Graph[_Node]: ...
+def induced_subgraph(G: Graph[_Node, _NodeData, _EdgeData], nbunch: _NBunch[_Node]) -> Graph[_Node, _NodeData, _EdgeData]: ...
 def edge_subgraph(G: Graph[_Node], edges): ...
 def restricted_view(G: Graph[_Node], nodes, edges): ...
 def to_directed(graph): ...
@@ -146,8 +146,8 @@ def selfloop_edges(
 ) -> Generator[tuple[_Node, _Node]]: ...
 @overload
 def selfloop_edges(
-    G: Graph[_Node], data: Literal[True], keys: Literal[False] = False, default=None
-) -> Generator[tuple[_Node, _Node, dict[str, Incomplete]]]: ...
+    G: Graph[_Node, _NodeData, _EdgeData], data: Literal[True], keys: Literal[False] = False, default=None
+) -> Generator[tuple[_Node, _Node, _EdgeData]]: ...
 @overload
 def selfloop_edges(
     G: Graph[_Node], data: str, keys: Literal[False] = False, default: _U | None = None
@@ -162,8 +162,8 @@ def selfloop_edges(
 ) -> Generator[tuple[_Node, _Node, int]]: ...
 @overload
 def selfloop_edges(
-    G: Graph[_Node], data: Literal[True], keys: Literal[True], default=None
-) -> Generator[tuple[_Node, _Node, int, dict[str, Incomplete]]]: ...
+    G: Graph[_Node, _NodeData, _EdgeData], data: Literal[True], keys: Literal[True], default=None
+) -> Generator[tuple[_Node, _Node, int, _EdgeData]]: ...
 @overload
 def selfloop_edges(
     G: Graph[_Node], data: str, keys: Literal[True], default: _U | None = None

stubs/networkx/networkx/classes/graph.pyi

@@ -1,34 +1,39 @@
-from collections.abc import Callable, Collection, Hashable, Iterable, Iterator, MutableMapping
+from collections.abc import Callable, Collection, Hashable, Iterable, Iterator, Mapping, MutableMapping
 from decimal import Decimal
 from functools import cached_property
-from typing import Any, ClassVar, TypeVar, overload
+from typing import Any, ClassVar, Generic, TypeVar, overload
 from typing_extensions import Self, TypeAlias
 
 import numpy
 from networkx.classes.coreviews import AdjacencyView, AtlasView
 from networkx.classes.digraph import DiGraph
 from networkx.classes.reportviews import DegreeView, DiDegreeView, EdgeView, NodeView, OutEdgeView
 
+_DataBound: TypeAlias = Mapping[str, Any]
+
 _Node = TypeVar("_Node", bound=Hashable)
-_NodeWithData: TypeAlias = tuple[_Node, dict[str, Any]]
-_NodePlus: TypeAlias = _Node | _NodeWithData[_Node]
+_NodeData = TypeVar("_NodeData", bound=_DataBound, default=dict[str, Any])
+_EdgeData = TypeVar("_EdgeData", bound=_DataBound, default=dict[str, Any])
+
+_NodeWithData: TypeAlias = tuple[_Node, _NodeData]
+_NodePlus: TypeAlias = _Node | _NodeWithData[_Node, _NodeData]
 _Edge: TypeAlias = tuple[_Node, _Node]
-_EdgeWithData: TypeAlias = tuple[_Node, _Node, dict[str, Any]]
-_EdgePlus: TypeAlias = _Edge[_Node] | _EdgeWithData[_Node]
+_EdgeWithData: TypeAlias = tuple[_Node, _Node, _EdgeData]
+_EdgePlus: TypeAlias = _Edge[_Node] | _EdgeWithData[_Node, _EdgeData]
 _MapFactory: TypeAlias = Callable[[], MutableMapping[str, Any]]
 _NBunch: TypeAlias = _Node | Iterable[_Node] | None
 _Data: TypeAlias = (
-    Graph[_Node]
-    | dict[_Node, dict[_Node, dict[str, Any]]]
+    Graph[_Node, _NodeData, _EdgeData]
+    | dict[_Node, dict[_Node, _NodeData]]
     | dict[_Node, Iterable[_Node]]
-    | Iterable[_EdgePlus[_Node]]
+    | Iterable[_EdgePlus[_Node, _EdgeData]]
     | numpy.ndarray[Any, Any]
     # | scipy.sparse.base.spmatrix
 )
 
 __all__ = ["Graph"]
 
-class Graph(Collection[_Node]):
+class Graph(Collection[_Node], Generic[_Node, _NodeData, _EdgeData]):
     __networkx_backend__: ClassVar[str]
     node_dict_factory: ClassVar[_MapFactory]
     node_attr_dict_factory: ClassVar[_MapFactory]
@@ -40,14 +45,16 @@ class Graph(Collection[_Node]):
     graph: dict[str, Any]
     __networkx_cache__: dict[str, Any]
 
-    def to_directed_class(self) -> type[DiGraph[_Node]]: ...
-    def to_undirected_class(self) -> type[Graph[_Node]]: ...
+    def to_directed_class(self) -> type[DiGraph[_Node, _NodeData, _EdgeData]]: ...
+    def to_undirected_class(self) -> type[Graph[_Node, _NodeData, _EdgeData]]: ...
     # @_dispatchable adds `backend` argument, but this decorated is unsupported constructor type here
     # and __init__() ignores this argument
     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
+    def __init__(
+        self, incoming_graph_data: _Data[_Node, _NodeData, _EdgeData] | None = None, **attr: Any
+    ) -> None: ...  # attr: key=value pairs
     @cached_property
-    def adj(self) -> AdjacencyView[_Node, _Node, dict[str, Any]]: ...
+    def adj(self) -> AdjacencyView[_Node, _Node, _EdgeData]: ...
     # This object is a read-only dict-like structure
     @property
     def name(self) -> str: ...
@@ -58,49 +65,53 @@ class Graph(Collection[_Node]):
     def __len__(self) -> int: ...
     def __getitem__(self, n: _Node) -> AtlasView[_Node, str, Any]: ...
     def add_node(self, node_for_adding: _Node, **attr: Any) -> None: ...  # attr: Set or change node attributes using key=value
-    def add_nodes_from(self, nodes_for_adding: Iterable[_NodePlus[_Node]], **attr: Any) -> None: ...  # attr: key=value pairs
+    def add_nodes_from(
+        self, nodes_for_adding: Iterable[_NodePlus[_Node, _NodeData]], **attr: Any
+    ) -> None: ...  # attr: key=value pairs
     def remove_node(self, n: _Node) -> None: ...
     def remove_nodes_from(self, nodes: Iterable[_Node]) -> None: ...
     @cached_property
-    def nodes(self) -> NodeView[_Node]: ...
+    def nodes(self) -> NodeView[_Node, _NodeData, _EdgeData]: ...
     def number_of_nodes(self) -> int: ...
     def order(self) -> int: ...
     def has_node(self, n: _Node) -> bool: ...
     # Including subtypes' possible return types for LSP
     def add_edge(self, u_of_edge: _Node, v_of_edge: _Node, **attr: Any) -> Hashable | None: ...
     # attr: Edge data (or labels or objects) can be assigned using keyword arguments
-    def add_edges_from(self, ebunch_to_add: Iterable[_EdgePlus[_Node]], **attr: Any) -> None: ...
+    def add_edges_from(self, ebunch_to_add: Iterable[_EdgePlus[_Node, _EdgeData]], **attr: Any) -> None: ...
     # attr: Edge data (or labels or objects) can be assigned using keyword arguments
     def add_weighted_edges_from(
         self, ebunch_to_add: Iterable[tuple[_Node, _Node, float | Decimal | None]], weight: str = "weight", **attr: Any
     ) -> None: ...
     # attr: Edge attributes to add/update for all edges.
     def remove_edge(self, u: _Node, v: _Node) -> None: ...
-    def remove_edges_from(self, ebunch: Iterable[_EdgePlus[_Node]]) -> None: ...
+    def remove_edges_from(self, ebunch: Iterable[_EdgePlus[_Node, _EdgeData]]) -> None: ...
     @overload
-    def update(self, edges: Graph[_Node], nodes: None = None) -> None: ...
+    def update(self, edges: Graph[_Node, _NodeData, _EdgeData], nodes: None = None) -> None: ...
     @overload
     def update(
-        self, edges: Graph[_Node] | Iterable[_EdgePlus[_Node]] | None = None, nodes: Iterable[_Node] | None = None
+        self,
+        edges: Graph[_Node, _NodeData, _EdgeData] | Iterable[_EdgePlus[_Node, _EdgeData]] | None = None,
+        nodes: Iterable[_Node] | None = None,
     ) -> None: ...
     def has_edge(self, u: _Node, v: _Node) -> bool: ...
     def neighbors(self, n: _Node) -> Iterator[_Node]: ...
     @cached_property
     # Including subtypes' possible return types for LSP
-    def edges(self) -> EdgeView[_Node] | OutEdgeView[_Node]: ...
-    def get_edge_data(self, u: _Node, v: _Node, default: Any = None) -> dict[str, Any]: ...
+    def edges(self) -> EdgeView[_Node, _NodeData, _EdgeData] | OutEdgeView[_Node, _NodeData, _EdgeData]: ...
+    def get_edge_data(self, u: _Node, v: _Node, default: Any = None) -> _EdgeData: ...
     # default:  any Python object
-    def adjacency(self) -> Iterator[tuple[_Node, dict[_Node, dict[str, Any]]]]: ...
+    def adjacency(self) -> Iterator[tuple[_Node, dict[_Node, _EdgeData]]]: ...
     @cached_property
     # Including subtypes' possible return types for LSP
-    def degree(self) -> DegreeView[_Node] | DiDegreeView[_Node]: ...
+    def degree(self) -> DegreeView[_Node, _NodeData, _EdgeData] | DiDegreeView[_Node, _NodeData, _EdgeData]: ...
     def clear(self) -> None: ...
     def clear_edges(self) -> None: ...
     def is_multigraph(self) -> bool: ...
     def is_directed(self) -> bool: ...
     def copy(self, as_view: bool = False) -> Self: ...
-    def to_directed(self, as_view: bool = False) -> DiGraph[_Node]: ...
-    def to_undirected(self, as_view: bool = False) -> Graph[_Node]: ...
+    def to_directed(self, as_view: bool = False) -> DiGraph[_Node, _NodeData, _EdgeData]: ...
+    def to_undirected(self, as_view: bool = False) -> Graph[_Node, _NodeData, _EdgeData]: ...
     def subgraph(self, nodes: _NBunch[_Node]) -> Self: ...
     def edge_subgraph(self, edges: Iterable[_Edge[_Node]]) -> Self: ...
     @overload