How deeply does Domain-Specific Small Language Models cover Louvain algorithm?

Defines the iterative move-and-aggregate procedure and notes the modularity-gain check, but stops short of a full formal proof.

Louvain algorithm

As explained in Domain-Specific Small Language Models

A community-detection algorithm that iteratively moves nodes and aggregates communities to improve a clustering objective.

A community-detection method that starts with each node alone, moves nodes between communities to improve a score, then aggregates the graph and repeats until improvement stops. section 14.1

Here it appears alongside GraphRAG indexing, where graph communities are computed after entities and relationships are merged into a NetworkX graph. section 14.1

Core steps shown here:

Begin from a singleton partition. section 14.1
Move nodes between communities to improve the quality score. section 14.1
Aggregate the resulting communities into a new network. section 14.1
Repeat until the score no longer improves. section 14.1

GraphRAG community detection

A knowledge graph is built from extracted entities and relationships, combined with NetworkX, then passed to the community routine before community summaries are generated.

How Domain-Specific Small Language Models covers it

Cover of Domain-Specific Small Language Models

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Also explained in

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Financial AI in PracticeA heuristic algorithm for detecting graph communities by optimizing modularity.
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Essential GraphRAGThe Louvain method is a graph community-detection algorithm that identifies communities by optimizing modularity.

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Related concepts

community detectionkind of · Community detection is a graph-analysis technique for identifying groups of nodes that are more densely or strongly connected to one another than to the rest of the graph.
graph clustering methodkind of · A graph clustering method groups vertices into clusters based on their connectivity, structural patterns, or related attributes.
label propagationalternative to · Label propagation is a graph-learning technique that iteratively propagates node labels through connections to assign labels or identify communities.
Leiden algorithmcontrasts with · The Leiden algorithm is a graph community-detection method that iteratively refines and aggregates partitions into well-connected communities while optimizing a quality function.
clusteringkind of · Clustering is the technique of grouping data items according to similarity or shared characteristics.
hierarchical community structureproduces · A hierarchical community structure represents graph communities at multiple levels of granularity, from broad groups to finer subcommunities.

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