Repository navigation
Preserve directed multigraphs in NetworkX edge exports - #1546
Merged
Merged
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
1 participant
Add this suggestion to a batch that can be applied as a single commit.This suggestion is invalid because no changes were made to the code.Suggestions cannot be applied while the pull request is closed.Suggestions cannot be applied while viewing a subset of changes.Only one suggestion per line can be applied in a batch.Add this suggestion to a batch that can be applied as a single commit.Applying suggestions on deleted lines is not supported.You must change the existing code in this line in order to create a valid suggestion.Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be applied on multi-line comments.Suggestions cannot be applied while the pull request is queued to merge.Suggestion cannot be applied right now. Please check back later.
NetworkX export converted a supplied MultiDiGraph into an undirected MultiGraph, making one-way edges traversable in reverse. The generic multigraph type check matched directed multigraphs first.
Choose MultiDiGraph before MultiGraph when constructing the selected-edge graph. Preserve graph direction, attributes, and source immutability; document the behavior.
Validation: 15 original regression failures and 47 passing controls; all 62 new cases pass after the fix. After rebasing onto current master, 134 related tests pass on Python 3.12. Prior Python 3.9 validation passed 128 cases with 6 optional Arrow skips. Tests cover all four NetworkX graph types, empty and partial selections, reciprocal edges, weights, node attributes, and multilayer edge queries.