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Strongly connected component

As explained in Graph Algorithms for Data Science

A strongly connected component is a maximal subgraph of a directed graph in which every node is reachable from every other node following relationship directions.

A strongly connected component (SCC) is a maximal subgraph of a directed graph where every node can reach every other node by following relationship directions. Unlike a weakly connected component, it does not ignore direction. section 5.3.2

SCC analysis reveals groups with mutual reachability and helps evaluate how directed-link changes affect access. It applies to follower networks, social groups, and one-way road systems. section 5.3.2

Road closure and reachability

If a road between intersections D and C closes, nodes D, E, F, and G can no longer reach A, B, or C. The network therefore separates into SCCs containing A, B, C and D, E, F, G. section 5.3.2

Listing 5.9 Executing the SCC algorithm on the follower network and storing the results as a followerScc node property
CALL gds.scc.write('follower-network', {writeProperty:'followerScc'})
YIELD componentCount, componentDistribution

Run the SCC algorithm on the follower network and store each node's component identifier with a Cypher procedure. section 5.3.2

The follower-network results can be summarized by component count and member distribution, then inspected through visualizations of large components. section 5.3.2

How Graph Algorithms for Data Science covers it

How deep it goes
Explains the definition, directionality, practical scenarios, GDS execution, stored results, and summary statistics without discussing algorithm internals.
Examples
Demonstrates SCCs with social networks, a one-way road network, follower-network statistics, and component visualizations.
What it assumes
Assumes directed graphs and relationship directions while distinguishing SCCs from direction-agnostic weakly connected components.
What you can do after
Operate a GDS SCC procedure, store component identifiers, and interpret component counts and member distributions.
How it connects
Connects directed graphs, reachability, weakly connected components, graph projections, and graph algorithms.
Cover of Graph Algorithms for Data Science
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This explainer was generated by Manning's knowledge graph from the text of Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.