Hi dear community,
I was wondering if there were some good practices about how you modelize
your graph when you have different kind of nodes (so differents labels)
that have properties in common.
Let me explain with an example :
Let's say I have a node containing localization data (city, country,
street, zipcode...) which is related to a "person" node.
I also have an other node, a "company", which have "countries" as a
property.
I can see many kind of modelization :
1. (person)-[:IS_LOCATED_IN]->(location {city:"", country:"france",
zipcode:""}), (company {countries: :["france", "USA"]})
2. (person)-[:IS_LOCATED_IN]->(location {city:"", country:"france",
zipcode:""}), (company)-[whatever]->(country {name:"france"})
3. (person)-[:IS_LOCATED_IN]->(location {city:"",
zipcode:""})-[whatever2]->(country {name:"france"})<-[whatever]-(company)
You will probably say that it depends on my needs, but I was just wondering
if there was a rule like "When you have data in common, you'd better
extract it to an other node and link them all to have a highely connected
graph".
Considering my needs, my application is not specifically focused on
localization, it is just some datas that could be share between nodes by
extracting them to node and create relations.
This question came to my mind after seeing some "time representation model"
like the GraphAware TimeTree.
- Is there a best modelization between the 3 I suggest ?
- Does it only depends on what will be the queries ?
- What about global Neo4j performances and disk(or memory) usage between
those 3 ?
- Do you have a better modelization ? :)
Thank you guyz
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