neo4j

问题 If I have a cypher query that unwinds a parameter, everything after that portion of the query is called x number of times of the unwind. I'd like to figure out a way to end the unwind and continue with other things. MATCH (thing:Thing) UNWIND { names } AS name CREATE thing-[:HAS_NAME]-(n:Name {name: name}) //done with the unwind WITH (thing) CREATE thing[:HAS_AGE]-(a:Age {age: 20}) In the above example, I will end up with two thing-[:HAS_AGE]->() relationships because of the unwind. Do I have

问题 I am using spring data rest to create an API over neo4j. I don't want to expose nodeId in my URLs, therefore I have a UUID instead. More info on here: How can I change neo4j Id to UUID and get finder methods to work? How can I modify the auto-generated links by the spring-data-rest to reflect the change to UUID instead of nodeId? Thanks ----UPDATED--- public class CustomBackendIdConverter implements BackendIdConverter { @Autowired PracticeAreaRepository practiceAreaRepository; @Override

问题 I created an application which embedded Neo4j. In that application I created and stored some nodes with some relationships. My application has saved this database to a file. I would like to visualise that data. I know I can see graphs if I fire up the Neo4j server but I do not know how to import my neo4j.db file into the Neo4j server so that I can visualise it. Any suggestions would be greatly appreciated. 回答1: Depending on your use case you might have different solutions: Use a web-based

问题 In the O'Reilly book "Graph Databases" in chapter 6, which is about how Neo4j stores a graph database it says: To understand why native graph processing is so much more efficient than graphs based on heavy indexing, consider the following. Depending on the implementation, index lookups could be O(log n) in algorithmic complexity versus O(1) for looking up immediate relationships. To traverse a network of m steps, the cost of the indexed approach, at O(m log n), dwarfs the cost of O(m) for an

问题 In the O'Reilly book "Graph Databases" in chapter 6, which is about how Neo4j stores a graph database it says: To understand why native graph processing is so much more efficient than graphs based on heavy indexing, consider the following. Depending on the implementation, index lookups could be O(log n) in algorithmic complexity versus O(1) for looking up immediate relationships. To traverse a network of m steps, the cost of the indexed approach, at O(m log n), dwarfs the cost of O(m) for an

问题 I can find the highest densely connected component in the graph using the code below: CALL algo.unionFind.stream('', ':pnHours', {}) YIELD nodeId,setId // groupBy setId, storing all node ids of the same set id into a list MATCH (node) where id(node) = nodeId WITH setId, collect(node) as nodes // order by the size of nodes list descending ORDER BY size(nodes) DESC LIMIT 1 // limiting to 3 RETURN nodes; But it does not help me visualize the topmost densely connected component (sub-graph)

问题 I can find the highest densely connected component in the graph using the code below: CALL algo.unionFind.stream('', ':pnHours', {}) YIELD nodeId,setId // groupBy setId, storing all node ids of the same set id into a list MATCH (node) where id(node) = nodeId WITH setId, collect(node) as nodes // order by the size of nodes list descending ORDER BY size(nodes) DESC LIMIT 1 // limiting to 3 RETURN nodes; But it does not help me visualize the topmost densely connected component (sub-graph)

问题 While importing a ton of data from spreadsheets, I attempted to use a labeling convention where nodes were capitalized like "This" and labels for relationships were labeled like "THIS". In one case, I accidentally used the relationship label format for a set of nodes. I then deleted those nodes and reimported them with the correct label format. (Side question - Was there a way to rename a label that I didn't see which could have avoided the delete/reimport?) My problem is that in the built-in

问题 I am working on a model using wikipedia topics' names for my experiments in full-text index. I set up and index on 'topic' (legacy), and do a full text search for : 'united states' : start n=node:topic('name:(united states)') return n The first results are not relevant at all: 'List of United States National Historic Landmarks in United States commonwealths and territories, associated states, and foreign states' [...] and the actual 'united states' is buried deep down the list. As such, it