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Weakly connected component

A weakly connected component is a maximal set of vertices in a directed graph that remains connected when edge directions are ignored.

How 2 Manning books explain it

Knowledge Graphs and LLMs in Action

A weakly connected component is a maximal set of vertices in a directed graph that remains connected when edge directions are ignored.

How it is used
Evaluates graph structure and resolves groups of related entity mentions in knowledge graphs.
How deep it goes
Explains projection, execution, stored identifiers, and distribution interpretation; defers dense community discovery to Louvain.
Examples
Uses a protein–protein interaction network and a person-mention similarity metagraph as concrete applications.
What you can do after
Apply it via Neo4j GDS to write component identifiers and inspect component distributions.

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Graph Algorithms for Data Science

A weakly connected component algorithm identifies maximal node sets that are connected when the directions of relationships are disregarded.

How it is used
Uses WCC early to assess graph connectivity, isolate disconnected regions, and focus later network analysis.
How deep it goes
Explains direction-insensitive connectivity and demonstrates write, stats, and stream workflows, without covering algorithm internals.
Examples
Demonstrates follower and retweet networks, component distributions, network visualizations, and self-retweet single-member components.
What you can do after
Operate/configure Neo4j GDS WCC procedures, store component identifiers, summarize sizes, and investigate disconnected or anomalous components.

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Related concepts

These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.