Question
Non-Deterministic Output from NetworkX Label Propagation
Neel Nova
0 reputation · 03 Sept 2020, 14:35 UTC
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0 reputation · 03 Sept 2020, 14:35 UTC
NetworkX's label_propagation_communities function (introduced in v2.6) offers a fast, near-linear-time approach to community detection. However, its tie-breaking mechanism relies on the global Python random module, and the public API does not expose a seed parameter. This means successive calls on the same graph can yield different partitions unless the user manually sets random.seed before each invocation.
This behavior raises a reproducibility concern for workflows that require stable community assignments. The documentation does not specify whether the algorithm is intended to be stochastic or if deterministic output is achievable through a supported interface. Additionally, the reliance on global state makes results sensitive to unrelated random calls elsewhere in the program.
What is the recommended pattern for obtaining reproducible results from this function? Is the lack of a seed parameter a deliberate design choice, or is there an undocumented way to control the randomness? How does this behavior vary across NetworkX versions?
34775 reputation · 03 Sept 2020, 17:09 UTC
NetworkX’s label_propagation_communities is non-deterministic by design. Repeated calls on the same graph can return different partitions. Reproducibility is achievable, but the supported mechanism depends on the NetworkX version you are running.
networkxrandom module for node ordering and tie-breaking.Label propagation is an asynchronous heuristic. Different node iteration orders and random tie-breaking lead to different local optima. The variation is therefore expected behavior of the heuristic, not a bug.
Version determines the correct call. The exact NetworkX version is the one missing diagnostic detail that changes the recommendation.
import networkx as nx; print(nx.__version__)import random; random.seed(0)Verification steps:
label_propagation_communities twice with the same seed/graph and compare the frozenset partitions for equality.Seeding reduces variation but does not guarantee identical output across NetworkX or Python versions due to internal implementation changes. Deterministic output means reproducible, not correct or unique; label propagation is a heuristic.
Do not assume global random.seed is sufficient in all releases. Some releases use an internal RNG instance. That is why the exact NetworkX version is needed to give a precise, supported call signature.
Use comments to ask for clarification. Post a solution as an answer.
34,775 reputation · 03 Sept 2020, 22:51 UTC
The algorithm chooses a random node order and breaks ties via the global random module. Without an exposed seed, successive calls on the same graph can return different partitions.
label_propagation_communities, set random.seed(some_value) and avoid other random calls until the function returns.Thus, reproducibility is achieved by controlling the global RNG rather than a built‑in option.