networkx: add all missing __all__ (#13956)

2026-05-06 21:43:59 +08:00 · 2025-05-08 22:25:25 -04:00
parent 41eb1a6731
commit 4b5e235d1c
226 changed files with 1014 additions and 1 deletions
@@ -1,6 +1,8 @@
 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]): ...
@_dispatchable
@@ -2,5 +2,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["average_clustering"]
+
@_dispatchable
 def average_clustering(G: Graph[_Node], trials: int = 1000, seed: int | RandomState | None = None): ...
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["local_node_connectivity", "node_connectivity", "all_pairs_node_connectivity"]
+
@_dispatchable
 def local_node_connectivity(G: Graph[_Node], source: _Node, target: _Node, cutoff: int | None = None): ...
@_dispatchable
@@ -2,5 +2,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["diameter"]
+
@_dispatchable
 def diameter(G: Graph[_Node], seed: int | RandomState | None = None): ...
@@ -1,6 +1,8 @@
 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): ...
@_dispatchable
@@ -1,5 +1,7 @@
 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): ...
@@ -1,5 +1,7 @@
 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]): ...
@@ -4,6 +4,8 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["randomized_partitioning", "one_exchange"]
+
@_dispatchable
 def randomized_partitioning(
    G: Graph[_Node], seed: int | RandomState | None = None, p: float = 0.5, weight: str | None = None
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["ramsey_R2"]
+
@_dispatchable
 def ramsey_R2(G: Graph[_Node]): ...
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["metric_closure", "steiner_tree"]
+
@_dispatchable
 def metric_closure(G: Graph[_Node], weight="weight"): ...
@_dispatchable
@@ -6,6 +6,15 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = [
+    "traveling_salesman_problem",
+    "christofides",
+    "asadpour_atsp",
+    "greedy_tsp",
+    "simulated_annealing_tsp",
+    "threshold_accepting_tsp",
+]
+
@_dispatchable
 def christofides(G: Graph[_Node], weight: str | None = "weight", tree: Graph[_Node] | None = None): ...
@_dispatchable
@@ -1,5 +1,7 @@
 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): ...
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__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
@@ -4,6 +4,13 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "degree_pearson_correlation_coefficient",
+    "degree_assortativity_coefficient",
+    "attribute_assortativity_coefficient",
+    "numeric_assortativity_coefficient",
+]
+
@_dispatchable
 def degree_assortativity_coefficient(
    G: Graph[_Node], x: str = "out", y: str = "in", weight: str | None = None, nodes: Iterable[Incomplete] | None = None
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["attribute_mixing_matrix", "attribute_mixing_dict", "degree_mixing_matrix", "degree_mixing_dict", "mixing_dict"]
+
@_dispatchable
 def attribute_mixing_dict(
    G: Graph[_Node], attribute: str, nodes: Iterable[Incomplete] | None = None, normalized: bool = False
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["average_neighbor_degree"]
+
@_dispatchable
 def average_neighbor_degree(
    G: Graph[_Node],
@@ -4,6 +4,8 @@ from collections.abc import Generator, Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["node_attribute_xy", "node_degree_xy"]
+
@_dispatchable
 def node_attribute_xy(
    G: Graph[_Node], attribute, nodes: Iterable[Incomplete] | None = None
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_at_free", "find_asteroidal_triple"]
+
@_dispatchable
 def find_asteroidal_triple(G: Graph[_Node]): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_bipartite", "is_bipartite_node_set", "color", "sets", "density", "degrees"]
+
@_dispatchable
 def color(G: Graph[_Node]): ...
@_dispatchable
@@ -1,6 +1,8 @@
 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): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["clustering", "average_clustering", "latapy_clustering", "robins_alexander_clustering"]
+
@_dispatchable
 def latapy_clustering(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None, mode: str = "dot"): ...

@@ -4,5 +4,7 @@ from collections.abc import Callable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["min_edge_cover"]
+
@_dispatchable
 def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None): ...
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 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: ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["maximal_extendability"]
+
@_dispatchable
 def maximal_extendability(G: Graph[_Node]): ...
@@ -5,6 +5,17 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = [
+    "configuration_model",
+    "havel_hakimi_graph",
+    "reverse_havel_hakimi_graph",
+    "alternating_havel_hakimi_graph",
+    "preferential_attachment_graph",
+    "random_graph",
+    "gnmk_random_graph",
+    "complete_bipartite_graph",
+]
+
@_dispatchable
 def complete_bipartite_graph(n1, n2, create_using: Graph[_Node] | None = None): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 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): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["biadjacency_matrix", "from_biadjacency_matrix"]
+
@_dispatchable
 def biadjacency_matrix(
    G: Graph[_Node],
@@ -4,6 +4,14 @@ from collections.abc import Callable, Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "projected_graph",
+    "weighted_projected_graph",
+    "collaboration_weighted_projected_graph",
+    "overlap_weighted_projected_graph",
+    "generic_weighted_projected_graph",
+]
+
@_dispatchable
 def projected_graph(B: Graph[_Node], nodes: Iterable[Incomplete], multigraph: bool = False): ...
@_dispatchable
@@ -4,5 +4,7 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["node_redundancy"]
+
@_dispatchable
 def node_redundancy(G: Graph[_Node], nodes: Iterable[Incomplete] | None = None): ...
@@ -1,5 +1,7 @@
 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"): ...
@@ -6,6 +6,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

 _U = TypeVar("_U")
+__all__ = ["edge_boundary", "node_boundary"]

@overload
 def edge_boundary(
@@ -4,6 +4,8 @@ from typing import overload
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["bridges", "has_bridges", "local_bridges"]
+
@_dispatchable
 def bridges(G: Graph[_Node], root: _Node | None = None) -> Generator[_Node, None, None]: ...
@_dispatchable
@@ -2,6 +2,8 @@ from networkx.classes.graph import Graph, _Edge, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["betweenness_centrality", "edge_betweenness_centrality"]
+
@_dispatchable
 def betweenness_centrality(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Edge, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["betweenness_centrality_subset", "edge_betweenness_centrality_subset"]
+
@_dispatchable
 def betweenness_centrality_subset(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["closeness_centrality", "incremental_closeness_centrality"]
+
@_dispatchable
 def closeness_centrality(
    G: Graph[_Node], u: _Node | None = None, distance=None, wf_improved: bool | None = True
@@ -2,6 +2,12 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = [
+    "current_flow_betweenness_centrality",
+    "approximate_current_flow_betweenness_centrality",
+    "edge_current_flow_betweenness_centrality",
+]
+
@_dispatchable
 def approximate_current_flow_betweenness_centrality(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["current_flow_betweenness_centrality_subset", "edge_current_flow_betweenness_centrality_subset"]
+
@_dispatchable
 def current_flow_betweenness_centrality_subset(
    G: Graph[_Node],
@@ -1,6 +1,8 @@
 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"): ...

@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["degree_centrality", "in_degree_centrality", "out_degree_centrality"]
+
@_dispatchable
 def degree_centrality(G: Graph[_Node]) -> dict[_Node, float]: ...
@_dispatchable
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["dispersion"]
+
@_dispatchable
 def dispersion(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["eigenvector_centrality", "eigenvector_centrality_numpy"]
+
@_dispatchable
 def eigenvector_centrality(
    G: Graph[_Node],
@@ -4,6 +4,15 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "group_betweenness_centrality",
+    "group_closeness_centrality",
+    "group_degree_centrality",
+    "group_in_degree_centrality",
+    "group_out_degree_centrality",
+    "prominent_group",
+]
+
@_dispatchable
 def group_betweenness_centrality(
    G: Graph[_Node], C, normalized: bool | None = True, weight: str | None = None, endpoints: bool | None = False
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["harmonic_centrality"]
+
@_dispatchable
 def harmonic_centrality(
    G: Graph[_Node], nbunch: Iterable[Incomplete] | None = None, distance=None, sources: Iterable[Incomplete] | None = None
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["katz_centrality", "katz_centrality_numpy"]
+
@_dispatchable
 def katz_centrality(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from collections.abc import Collection
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["laplacian_centrality"]
+
@_dispatchable
 def laplacian_centrality(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["percolation_centrality"]
+
@_dispatchable
 def percolation_centrality(
    G: Graph[_Node],
@@ -4,6 +4,8 @@ from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["global_reaching_centrality", "local_reaching_centrality"]
+
@_dispatchable
 def global_reaching_centrality(G: DiGraph[_Node], weight: str | None = None, normalized: bool | None = True): ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["second_order_centrality"]
+
@_dispatchable
 def second_order_centrality(G: Graph[_Node], weight: str | None = "weight"): ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["subgraph_centrality_exp", "subgraph_centrality", "communicability_betweenness_centrality", "estrada_index"]
+
@_dispatchable
 def subgraph_centrality_exp(G: Graph[_Node]): ...
@_dispatchable
@@ -2,6 +2,8 @@ from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["trophic_levels", "trophic_differences", "trophic_incoherence_parameter"]
+
@_dispatchable
 def trophic_levels(G: DiGraph[_Node], weight="weight"): ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["voterank"]
+
@_dispatchable
 def voterank(G: Graph[_Node], number_of_nodes: int | None = None): ...
@@ -3,5 +3,7 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["chain_decomposition"]
+
@_dispatchable
 def chain_decomposition(G: Graph[_Node], root: _Node | None = None) -> Generator[list[tuple[_Node, _Node]], None, None]: ...
@@ -1,10 +1,20 @@
 import sys
+from _typeshed import Incomplete
 from collections.abc import Generator

 from networkx.classes.graph import Graph, _Node
 from networkx.exception import NetworkXException
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "is_chordal",
+    "find_induced_nodes",
+    "chordal_graph_cliques",
+    "chordal_graph_treewidth",
+    "NetworkXTreewidthBoundExceeded",
+    "complete_to_chordal_graph",
+]
+
 class NetworkXTreewidthBoundExceeded(NetworkXException): ...

@_dispatchable
@@ -15,3 +25,5 @@ def find_induced_nodes(G: Graph[_Node], s: _Node, t: _Node, treewidth_bound: flo
 def chordal_graph_cliques(G: Graph[_Node]) -> Generator[frozenset[_Node], None, None]: ...
@_dispatchable
 def chordal_graph_treewidth(G: Graph[_Node]) -> int: ...
+@_dispatchable
+def complete_to_chordal_graph(G) -> tuple[Incomplete, dict[Incomplete, int]]: ...
@@ -5,6 +5,17 @@ from typing import overload
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "find_cliques",
+    "find_cliques_recursive",
+    "make_max_clique_graph",
+    "make_clique_bipartite",
+    "node_clique_number",
+    "number_of_cliques",
+    "enumerate_all_cliques",
+    "max_weight_clique",
+]
+
@_dispatchable
 def enumerate_all_cliques(G: Graph[_Node]) -> Generator[list[_Node], None, None]: ...
@_dispatchable
@@ -23,3 +34,6 @@ 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]: ...
+@_dispatchable
+def max_weight_clique(G, weight="weight") -> tuple[Incomplete, Incomplete]: ...
@@ -3,6 +3,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["triangles", "average_clustering", "clustering", "transitivity", "square_clustering", "generalized_degree"]
+
@_dispatchable
 def triangles(G: Graph[_Node], nodes=None): ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["equitable_color"]
+
@_dispatchable
 def equitable_color(G: Graph[_Node], num_colors): ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["communicability", "communicability_exp"]
+
@_dispatchable
 def communicability(G: Graph[_Node]): ...
@_dispatchable
@@ -2,5 +2,7 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["asyn_fluidc"]
+
@_dispatchable
 def asyn_fluidc(G: Graph[_Node], k: int, max_iter: int = 100, seed: int | RandomState | None = None): ...
@@ -4,6 +4,8 @@ from collections.abc import Callable, Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["girvan_newman"]
+
@_dispatchable
 def girvan_newman(
    G: Graph[_Node], most_valuable_edge: Callable[..., Incomplete] | None = None
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_partition"]
+
@_dispatchable
 def is_partition(G: Graph[_Node], communities): ...
@@ -4,5 +4,7 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["k_clique_communities"]
+
@_dispatchable
 def k_clique_communities(G: Graph[_Node], k: int, cliques=None) -> Generator[Incomplete, None, None]: ...
@@ -4,6 +4,8 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["kernighan_lin_bisection"]
+
@_dispatchable
 def kernighan_lin_bisection(
    G: Graph[_Node],
@@ -5,6 +5,10 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 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]: ...
@_dispatchable
 def asyn_lpa_communities(
    G: Graph[_Node], weight: str | None = None, seed: int | RandomState | None = None
@@ -5,6 +5,8 @@ from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable
 from numpy.random import RandomState

+__all__ = ["louvain_communities", "louvain_partitions"]
+
@_dispatchable
 def louvain_communities(
    G: Graph[_Node],
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["lukes_partitioning"]
+
@_dispatchable
 def lukes_partitioning(G: Graph[_Node], max_size: int, node_weight=None, edge_weight=None): ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["greedy_modularity_communities", "naive_greedy_modularity_communities"]
+
@_dispatchable
 def greedy_modularity_communities(
    G: Graph[_Node], weight: str | None = None, resolution: float | None = 1, cutoff: int | None = 1, best_n: int | None = None
@@ -3,6 +3,8 @@ from collections.abc import Generator

 from networkx.utils.backends import _dispatchable

+__all__ = ["number_attracting_components", "attracting_components", "is_attracting_component"]
+
@_dispatchable
 def attracting_components(G) -> Generator[Incomplete, None, None]: ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["biconnected_components", "biconnected_component_edges", "is_biconnected", "articulation_points"]
+
@_dispatchable
 def is_biconnected(G: Graph[_Node]): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["number_connected_components", "connected_components", "is_connected", "node_connected_component"]
+
@_dispatchable
 def connected_components(G: Graph[_Node]) -> Generator[Incomplete, None, None]: ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_semiconnected"]
+
@_dispatchable
 def is_semiconnected(G: Graph[_Node]): ...
@@ -4,6 +4,14 @@ from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "number_strongly_connected_components",
+    "strongly_connected_components",
+    "is_strongly_connected",
+    "kosaraju_strongly_connected_components",
+    "condensation",
+]
+
@_dispatchable
 def strongly_connected_components(G: Graph[_Node]) -> Generator[set[_Node], None, None]: ...
@_dispatchable
@@ -3,6 +3,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["number_weakly_connected_components", "weakly_connected_components", "is_weakly_connected"]
+
@_dispatchable
 def weakly_connected_components(G: Graph[_Node]) -> Generator[set[_Node], None, None]: ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["k_edge_augmentation", "is_k_edge_connected", "is_locally_k_edge_connected"]
+
@_dispatchable
 def is_k_edge_connected(G: Graph[_Node], k: int): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["k_edge_components", "k_edge_subgraphs", "bridge_components", "EdgeComponentAuxGraph"]
+
@_dispatchable
 def k_edge_components(G: Graph[_Node], k: int): ...
@_dispatchable
@@ -1,5 +1,7 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["stoer_wagner"]
+
@_dispatchable
 def stoer_wagner(G: Graph[_Node], weight: str = "weight", heap: type = ...): ...
@@ -1,5 +1,7 @@
 from networkx.utils.backends import _dispatchable

+__all__ = ["build_auxiliary_node_connectivity", "build_auxiliary_edge_connectivity"]
+
@_dispatchable
 def build_auxiliary_node_connectivity(G): ...
@_dispatchable
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["core_number", "k_core", "k_shell", "k_crust", "k_corona", "k_truss", "onion_layers"]
+
@_dispatchable
 def core_number(G: Graph[_Node]): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Callable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["min_edge_cover", "is_edge_cover"]
+
@_dispatchable
 def min_edge_cover(G: Graph[_Node], matching_algorithm: Callable[..., Incomplete] | None = None): ...
@_dispatchable
@@ -3,6 +3,17 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "boundary_expansion",
+    "conductance",
+    "cut_size",
+    "edge_expansion",
+    "mixing_expansion",
+    "node_expansion",
+    "normalized_cut_size",
+    "volume",
+]
+
@_dispatchable
 def cut_size(G: Graph[_Node], S: Iterable[_Node], T: Iterable[_Node] | None = None, weight: str | None = None): ...
@_dispatchable
@@ -5,6 +5,16 @@ from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "cycle_basis",
+    "simple_cycles",
+    "recursive_simple_cycles",
+    "find_cycle",
+    "minimum_cycle_basis",
+    "chordless_cycles",
+    "girth",
+]
+
@_dispatchable
 def cycle_basis(G: Graph[_Node], root: _Node | None = None): ...
@_dispatchable
@@ -24,3 +34,5 @@ def recursive_simple_cycles(G: DiGraph[_Node]): ...
 def find_cycle(G: Graph[_Node], source=None, orientation=None): ...
@_dispatchable
 def minimum_cycle_basis(G: Graph[_Node], weight: str | None = None): ...
+@_dispatchable
+def girth(G) -> float | int: ...
@@ -1,7 +1,15 @@
+from _typeshed import Incomplete
+
 from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_d_separator", "is_minimal_d_separator", "find_minimal_d_separator", "d_separated", "minimal_d_separator"]
+
+@_dispatchable
+def is_d_separator(G, x, y, z) -> bool: ...
+@_dispatchable
+def find_minimal_d_separator(G, x, y, *, included=None, restricted=None) -> set[Incomplete] | None: ...
@_dispatchable
 def d_separated(G, x, y, z): ...
@_dispatchable
@@ -5,6 +5,25 @@ from networkx.classes.digraph import DiGraph
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "descendants",
+    "ancestors",
+    "topological_sort",
+    "lexicographical_topological_sort",
+    "all_topological_sorts",
+    "topological_generations",
+    "is_directed_acyclic_graph",
+    "is_aperiodic",
+    "transitive_closure",
+    "transitive_closure_dag",
+    "transitive_reduction",
+    "antichains",
+    "dag_longest_path",
+    "dag_longest_path_length",
+    "dag_to_branching",
+    "compute_v_structures",
+]
+
@_dispatchable
 def descendants(G: Graph[_Node], source) -> set[_Node]: ...
@_dispatchable
@@ -42,3 +61,5 @@ def dag_longest_path(
 def dag_longest_path_length(G: DiGraph[_Node], weight: str | None = "weight", default_weight: int | None = 1) -> int: ...
@_dispatchable
 def dag_to_branching(G: Graph[_Node]) -> Graph[_Node]: ...
+@_dispatchable
+def compute_v_structures(G) -> Generator[tuple[Incomplete, Incomplete, Incomplete]]: ...
@@ -1,11 +1,26 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "eccentricity",
+    "diameter",
+    "harmonic_diameter",
+    "radius",
+    "periphery",
+    "center",
+    "barycenter",
+    "resistance_distance",
+    "kemeny_constant",
+    "effective_graph_resistance",
+]
+
@_dispatchable
 def eccentricity(G: Graph[_Node], v: _Node | None = None, sp=None, weight: str | None = None): ...
@_dispatchable
 def diameter(G: Graph[_Node], e=None, usebounds=False, weight: str | None = None): ...
@_dispatchable
+def harmonic_diameter(G, sp=None) -> float: ...
+@_dispatchable
 def periphery(G: Graph[_Node], e=None, usebounds=False, weight: str | None = None): ...
@_dispatchable
 def radius(G: Graph[_Node], e=None, usebounds=False, weight: str | None = None): ...
@@ -15,3 +30,7 @@ def center(G: Graph[_Node], e=None, usebounds=False, weight: str | None = None):
 def barycenter(G, weight: str | None = None, attr=None, sp=None): ...
@_dispatchable
 def resistance_distance(G: Graph[_Node], nodeA=None, nodeB=None, weight: str | None = None, invert_weight: bool = True): ...
+@_dispatchable
+def effective_graph_resistance(G, weight=None, invert_weight=True) -> float: ...
+@_dispatchable
+def kemeny_constant(G, *, weight=None) -> float: ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_distance_regular", "is_strongly_regular", "intersection_array", "global_parameters"]
+
@_dispatchable
 def is_distance_regular(G: Graph[_Node]): ...
@_dispatchable
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["immediate_dominators", "dominance_frontiers"]
+
@_dispatchable
 def immediate_dominators(G: Graph[_Node], start: _Node): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Iterable
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["dominating_set", "is_dominating_set"]
+
@_dispatchable
 def dominating_set(G: Graph[_Node], start_with: _Node | None = None): ...
@_dispatchable
@@ -1,6 +1,8 @@
 from networkx.classes.graph import _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["efficiency", "local_efficiency", "global_efficiency"]
+
@_dispatchable
 def efficiency(G, u: _Node, v: _Node): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["is_eulerian", "eulerian_circuit", "eulerize", "is_semieulerian", "has_eulerian_path", "eulerian_path"]
+
@_dispatchable
 def is_eulerian(G: Graph[_Node]): ...
@_dispatchable
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["boykov_kolmogorov"]
+
@_dispatchable
 def boykov_kolmogorov(
    G: Graph[_Node],
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["capacity_scaling"]
+
@_dispatchable
 def capacity_scaling(
    G: Graph[_Node], demand: str = "demand", capacity: str = "capacity", weight: str = "weight", heap: type = ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["dinitz"]
+
@_dispatchable
 def dinitz(
    G: Graph[_Node],
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["edmonds_karp"]
+
@_dispatchable
 def edmonds_karp(
    G: Graph[_Node],
@@ -3,6 +3,8 @@ from _typeshed import Incomplete, SupportsGetItem
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["min_cost_flow_cost", "min_cost_flow", "cost_of_flow", "max_flow_min_cost"]
+
@_dispatchable
 def min_cost_flow_cost(G: Graph[_Node], demand: str = "demand", capacity: str = "capacity", weight: str = "weight"): ...
@_dispatchable
@@ -4,6 +4,8 @@ from collections.abc import Generator
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["network_simplex"]
+
 class _DataEssentialsAndFunctions:
    node_list: Incomplete
    node_indices: Incomplete
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["preflow_push"]
+
@_dispatchable
 def preflow_push(
    G: Graph[_Node],
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["shortest_augmenting_path"]
+
@_dispatchable
 def shortest_augmenting_path(
    G: Graph[_Node],
@@ -2,6 +2,8 @@ from _typeshed import Incomplete

 from networkx.utils.backends import _dispatchable

+__all__ = ["CurrentEdge", "Level", "GlobalRelabelThreshold", "build_residual_network", "detect_unboundedness", "build_flow_dict"]
+
 class CurrentEdge:
    def __init__(self, edges) -> None: ...
    def get(self): ...
@@ -1,6 +1,8 @@
 from networkx.classes.graph import Graph, _Node
 from networkx.utils.backends import _dispatchable

+__all__ = ["weisfeiler_lehman_graph_hash", "weisfeiler_lehman_subgraph_hashes"]
+
@_dispatchable
 def weisfeiler_lehman_graph_hash(
    G: Graph[_Node],
@@ -3,6 +3,15 @@ from collections.abc import Iterable

 from networkx.utils.backends import _dispatchable

+__all__ = [
+    "is_graphical",
+    "is_multigraphical",
+    "is_pseudographical",
+    "is_digraphical",
+    "is_valid_degree_sequence_erdos_gallai",
+    "is_valid_degree_sequence_havel_hakimi",
+]
+
@_dispatchable
 def is_graphical(sequence: Iterable[Incomplete], method="eg"): ...
@_dispatchable
@@ -1,4 +1,6 @@
 from networkx.utils.backends import _dispatchable

+__all__ = ["flow_hierarchy"]
+
@_dispatchable
 def flow_hierarchy(G, weight: str | None = None): ...
--- a/Show More
+++ b/Show More