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FalkorDB · graph “domino”
2,073 nodes and 11,573 relationships: a curated medication knowledge graph, the documented cascade patterns, the synthetic patients, and everything the agents have written back (runs, findings, tasks, decisions).
The whole medication knowledge graph from FalkorDB, in 3D: each drug, each side effect or diagnosis, and every CAUSES and TREATED_BY edge between them. Light up a patient’s cascade and see where it runs through everything else.
Two steps of CAUSES and TREATED_BY around any drug or effect. Click a node to move there.
allShortestPaths over CAUSES and TREATED_BY: every shortest cascade route between two drugs.
Run any read query from the catalog against the live graph. Parameters are validated and sent separately; the database runs it as GRAPH.RO_QUERY.
Variable-length walk over CAUSES and TREATED_BY edges that stays inside the patient’s medication list: drug → effect → drug → effect → drug, up to three drug-to-drug hops. It starts only from drugs with at least one direct link, which cuts the search by about five times.
MATCH (p:Patient {id: $pid})-[:TAKES]->(m:Drug)
WITH p, collect(m.id) AS meds
MATCH (p)-[:TAKES]->(a:Drug)-[:CAUSES]->(:Effect)-[:TREATED_BY]->(:Drug)<-[:TAKES]-(p)
WITH DISTINCT a, meds
MATCH path = (a)-[:CAUSES|TREATED_BY*2..6]->(z:Drug)
WHERE z <> a AND all(n IN nodes(path) WHERE NOT n:Drug OR n.id IN meds)
RETURN [r IN relationships(path) | [startNode(r).id, type(r), endNode(r).id]] AS hopsData model