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| Original file line number | Diff line number | Diff line change |
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| @@ -1,9 +1,11 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["min_weighted_dominating_set", "min_edge_dominating_set"] | ||
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| @_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]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,10 @@ | ||
| from _typeshed import Incomplete | ||
| from collections import defaultdict | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["k_components"] | ||
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| @_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]]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,9 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["min_maximal_matching"] | ||
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| @_dispatchable | ||
| def min_maximal_matching(G: Graph[_Node]): ... | ||
| def min_maximal_matching(G: Graph[_Node]) -> set[Incomplete]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,9 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["min_weighted_vertex_cover"] | ||
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| @_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]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,11 +1,13 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["degree_centrality", "betweenness_centrality", "closeness_centrality"] | ||
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| @_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]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,9 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["spectral_bipartivity"] | ||
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| @_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]: ... |
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,9 +1,13 @@ | ||
| from _typeshed import Incomplete | ||
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| from networkx.classes.graph import Graph, _Node | ||
| from networkx.utils.backends import _dispatchable | ||
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| __all__ = ["current_flow_closeness_centrality", "information_centrality"] | ||
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| @_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]: ... | ||
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| information_centrality = current_flow_closeness_centrality |
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Choose a reason Spam Abuse Off Topic Outdated Duplicate Resolved Low QualitySo, should we return Any?
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Choose a reason Spam Abuse Off Topic Outdated Duplicate Resolved Low QualityI think I added this comment initially because I probably added Any as well, but I think you could actually represent the return type with overloads based on the format param. So it's more "incomplete", than "unrepresentable".
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