That can be non-obvious, so fair. We (graphistry) get pulled into a lot of investigative scenarios -- account takeover (web logs), malware/phishing analysis (ho

That can be non-obvious, so fair. We (graphistry) get pulled into a lot of investigative scenarios -- account takeover (web logs), malware/phishing analysis (host/network logs & feeds), AML, claims fraud, etc. I found the problems being solved to be some combination of: awkward to express with SQL, too slow to run in a RDBMS, or hard to visually explore relationships/correlations. Examples: === Shortest Paths 1a. Referral: "Who on our team connected to which leadership at Apple?" (target:Company[name="Apple"])<-[_:Leadership]--(champion)--[]-->(us:Company[name="myCompany"]) 1b. Supply Chain, AML, entanglements...: "How are these companies related, even if 5 companies away, and across all sorts of relationship types?" (a:Company[name="a16z"])-[r:1..3]-(b:Company[name="juicero"]) === Neighborhood (incl. multi-hop): 2a: 360 context on a security/fraud/ops incident: (hacked:Computer[ip="10.10.0.0"])-[e:Alert]-(metadata:) + (hacked:Computer[ip="10.10.0.0"])-[Login]->(u:User)-[e:Alert]-(metadata:) 2b: fraud rings: (fraudster:clientIP)-[login:http]-(b:Fingerprint) + (fraudster:clientIP)-[x:http[method="POST"]]-(p:Page) 2c: Journeys (customer, patient, ...) (a:Patient[id=123]-[e:1..2]->(b:) === Whole system optimization / compute: Personalized pagerank, supplychain optimization, business process mining, ... === The above can be extended, such as by adding in compute (correlation, influence scores, ...). That feds into viz / recommendations / decision making. or: Not all uses of graph are end-to-end. We often get used with a graph db to improve understanding it (our viz scale 100-1000X over the tools here via GPUs)... but folks may instead plug their graphdb into a tabular frontend. Or use us with a tabular system like Splunk/Spark/Elastic. So the above can be hard to write in Splunk/SQL, or slow to run, or hard to visually understand.