7. pagerank - over a caller-supplied node set

Computes PageRank scores over a subgraph that you define via nodes=. The engine restricts the iteration to that node universe and the edges between them; everything outside is ignored. Returns a { pk → score } map summing to 1.

• Influence ranking inside a tenant / project / topic - "who matters in this subgraph?"
• Recommendation seeds - score a candidate set, take the top-K.
• Any case where the full graph is too big to rank globally, but a slice is the right scope.

The relation must be declared in the schema's [[relations]] block. See schemas/reference#relations.

nodes is a comma-separated list of the primary keys that form the universe. Edges to keys outside the list are dropped; the iteration is scoped to what you pass.

curl -G "https://$OC_HOST/v1/tenants/$OC_TENANT/graph/shop.orders/pagerank" \
  --data-urlencode "rel=bought_product" \
  --data-urlencode "nodes=o001,o002,o003,o004,o005" \
  -H "Authorization: Bearer $OC_TOKEN"

result = db.graph.pagerank(
    "shop.orders",
    rel="bought_product",
    nodes=["o001", "o002", "o003", "o004", "o005"],
)

const result = await db.graph.pagerank("shop.orders", {
  rel: "bought_product",
  nodes: ["o001", "o002", "o003", "o004", "o005"],
});

result, _ := db.Graph().PageRank(ctx, "shop.orders", originchain.PageRankRequest{
    Rel:   "bought_product",
    Nodes: []string{"o001", "o002", "o003", "o004", "o005"},
})

{
  "o001": 0.241,
  "o002": 0.198,
  "o003": 0.187,
  "o004": 0.192,
  "o005": 0.182
}

A map from primary key to score. Scores sum to 1 across the supplied node set. Sort client-side for "top-K influencers".

• Power-iteration PageRank with damping factor 0.85.
• The induced subgraph is built once from the nodes list; iteration converges on that subgraph only.
• Caller-scoped universe is intentional - the engine refuses to rank the entire tenant graph because that's almost never what you want and is unbounded in cost.