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A decision guide for choosing between Neo4j GDS Louvain and custom Cypher for community detection, covering performance trade-offs, memory constraints, and implementation.
Deploy a single Neo4j 5.x instance with Graph Data Science for fast community detection on millions of nodes. This guide covers requirements, minimal design, trust boundaries, operational checks, failure modes, and scaling triggers.
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
neo4j
networkx