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Graph Algorithms

Uni includes a built-in graph algorithm library exposed through Cypher procedures. Use it to compute centrality, clustering, paths, and connectivity without exporting data.

What It Provides

  • Centrality: PageRank, betweenness, closeness, eigenvector, Katz.
  • Connectivity: WCC, SCC, bridges, articulation points.
  • Paths: shortest paths, k-shortest, A*, BFS/DFS.
  • Community detection: Louvain, label propagation.

Example

use uni_db::Uni;

# async fn demo() -> Result<(), uni_db::UniError> {
let db = Uni::open("./my_db").build().await?;

let rows = db.query(
    "CALL uni.algo.pageRank() YIELD nodeId, score RETURN nodeId, score ORDER BY score DESC LIMIT 10"
).await?;

println!("{:?}", rows);
# Ok(())
# }

import uni_db

db = uni_db.Database("./my_db")
rows = db.query(
    "CALL uni.algo.pageRank() YIELD nodeId, score RETURN nodeId, score ORDER BY score DESC LIMIT 10"
)
print(rows)

Use Cases

  • Identify important nodes in a graph.
  • Detect communities and clusters.
  • Compute routing or similarity metrics.

When To Use

Use built-in algorithms when you need graph analytics without exporting data or building your own pipeline.