I am completely new to NEO4j and using it for the first time ever now for my masters program. Ive read the documentation and watched tutorials online but can’t seem to figure out how I can represent my nodes in the way I want.
I have a dataframe with 3 columns, the first represents a page name, the second also represents a page name, and the third represents a similarity score between those two pages. How can I create a graph in NEO4J where the nodes are my unique page names and the relationships between nodes are drawn if there is a similarity score between them (so if the sim-score is 0 they don’t draw a relationship)? I want to show the similarity score as the text of the relationship.
Furthermore, I want to know if there is an easy way to figure out which node had the most relationships to other nodes?
I’ve added a screenshot of the header of my DF for clarity https://imgur.com/a/pg0knh6. I hope anyone can help me, thanks in advance!
Edit: What I have tried
LOAD CSV WITH HEADERS FROM 'file:///wiki-small.csv' AS line
MERGE (p:Page {name: line.First})
MERGE (p2:Page {name: line.Second})
MERGE (p)-[r:SIMILAR]->(p2)
ON CREATE SET r.similarity = toFloat(line.Sim)
Next block to remove the similarities relationships which are 0
MATCH ()-[r:SIMILAR]->() WHERE r.Sim=0
DELETE r
This works partially. As in it gives me the correct structure of the nodes but doesn't give me the similarity scores as relationship labels. I also still need to figure out how I can find the node with the most connections.
For the first question:
How can I create a graph in NEO4J where the nodes are my unique page names and the relationships between nodes are drawn if there is a similarity score between them (so if the sim-score is 0 they don’t draw a relationship)?
I think a better approach is to remove in advance the rows with similarity = 0.0 before ingesting them into Neo4j. Could it be something feasible? If your dataset is not so big, I think it is very fast to do in Python. Otherwise the solution you provide of deleting after inserting the data is an option.
In case of a big dataset, maybe it's better if you load the data using apoc.periodic.iterate or USING PERIODIC COMMIT.
Second question
I want to know if there is an easy way to figure out which node had the most relationships to other nodes?
This is an easy query. Again, you can do it with play Cypher or using APOC library:
# Plain Cypher
MATCH (n:Page)-[r:SIMILAR]->()
RETURN n.name, count(*) as cat
ORDER BY cnt DESC
# APOC
MATCH (n:Page)
RETURN apoc.node.degree(n, "SIMILAR>") AS output;
EDIT
To display the similarity scores, in Neo4j Desktop or in the others web interfaces, you can simply: click on a SIMILARITY arrow --> on the top of the running cell the labels are shown, click on the SIMILAR label marker --> on the bottom of the running cell, at the right of Caption, select the property that you want to show (similarity in your case)
Then all the arrows are displayed with the similarity score
To the second question: I think you should keep a clear separation between the way you store data and the way you visualize it. Having the similarity score (a property of the SIMILARITY edge) as a "label" is something that is best dealt with by using an adequate viz library or platform. Ours (Graphileon) could be such a platform, although there are also others.
We offer the possibility to "style" the edges with so-called selectors like
"label":"(%).property.simScore" that would use the simScore as a label. On top of that you could do thing like
"width":"evaluate((%).properties.simScore < 0.500 ? 3 : 10)"
or
"fillColor":"evaluate((%).properties.simScore < 0.500 ? grey : red)"
to distinguish visually high simScores.
Full disclosure : I work for Graphileon.
Related
I have 565 articles in Neo4j and I ran LPA to obtain clusters.
I have the following result: 69 communities.
I would like to display all the communities at the same time in Neo4j.
I tried several Cypher queries with the property key 'community' but it didn't work.
My data looks like this:
How can I do it ?
Presumably, you are using the neo4j Browser to visualize your results.
When your Cypher query returns any nodes, relationships, or paths, the browser will automatically show you the Graph view (on the left side of the result panel, you should see icons with captions that may include Graph, Table, Text, etc.). The Graph view only shows nodes and relationships, and not anything else that was returned.
However, if you click on the other icons (say, Table or Text), you should see more results -- like the communities, presented in different formats.
By the way, specifying the node label would make your query more efficient (and adding an index would make it even more efficient if you have a lot of ARTICLE nodes):
MATCH (n:ARTICLE) WHERE EXISTS(n.community)
RETURN n, n.community
I would like to apply label propagation to my data in Neo4j. My data looks like the image.
The relationship 'Appears_in' has the weight property and some articles nodes has seed label property.
I would like to propagate this seed labels to create clusters with the articles that speaks about the same topic, for example (politics cluster). More precisely I would like to propagate seed label to another article node but articles do not have a direct relationship between them. They are 'somewhere' connected through the words that they have in commun...
Is it possible to propagate the label from one article node to another article node through the words' node?
Yes, this is quite a frequent scenario. You want to project a bipartite graph to a monopartite graph. I wrote a blog post about a very similar scenario.
I will give you a solution that handles all in one step. It consists of two parts. We project a bipartite graph to a monopartite with the cypher projection. In the next step, you run the Label Propagation algorithm. You will be needing the Neo4j Graph Data Science library. Here is an example query:
CALL gds.labelPropagation.write({
nodeQuery:"MATCH (n:Article) RETURN id(n) as id, n.seed_property as seed",
relationshipQuery:"MATCH (a:Article)<-[:APPEARS_IN]-()-[:APPEARS_IN]->(b:Article)
RETURN id(a) as source, id(b) as target",
seedProperty: "seed",
writeProperty: "lpa"
})
I didn't include the relationship weights as I don't know exactly how you want them handled. You could also use any of the similarity algorithms instead to project a monopartite graph.
Hope this helps
The answer to this question shows how to get a list of all nodes connected to a particular node via a path of known relationship types.
As a follow up to that question, I'm trying to determine if traversing the graph like this is the most efficient way to get all nodes connected to a particular node via any path.
My scenario: I have a tree of groups (group can have any number of children). This I model with IS_PARENT_OF relationships. Groups can also relate to any other groups via a special relationship called role playing. This I model with PLAYS_ROLE_IN relationships.
The most common question I want to ask is MATCH(n {name: "xxx") -[*]-> (o) RETURN o.name, but this seems to be extremely slow on even a small number of nodes (4000 nodes - takes 5s to return an answer). Note that the graph may contain cycles (n-IS_PARENT_OF->o, n<-PLAYS_ROLE_IN-o).
Is connectedness via any path not something that can be indexed?
As a first point, by not using labels and an indexed property for your starting node, this will already need to first find ALL the nodes in the graph and opening the PropertyContainer to see if the node has the property name with a value "xxx".
Secondly, if you now an approximate maximum depth of parentship, you may want to limit the depth of the search
I would suggest you add a label of your choice to your nodes and index the name property.
Use label, e.g. :Group for your starting point and an index for :Group(name)
Then Neo4j can quickly find your starting point without scanning the whole graph.
You can easily see where the time is spent by prefixing your query with PROFILE.
Do you really want all arbitrarily long paths from the starting point? Or just all pairs of connected nodes?
If the latter then this query would be more efficient.
MATCH (n:Group)-[:IS_PARENT_OF|:PLAYS_ROLE_IN]->(m:Group)
RETURN n,m
I was reading a book recommended on Neo4j site: http://neo4j.com/books/graph-databases/ about graph database performance and it said:
"In contrast to relational databases, where join-intensive query performance deteriorates
as the dataset gets bigger, with a graph database performance tends to remain
relatively constant, even as the dataset grows. This is because queries are localized to a
portion of the graph. As a result, the execution time for each query is proportional only
to the size of the part of the graph traversed to satisfy that query, rather than the size of
the overall graph."
So e.g. I want to return only nodes with a label "Doctor, that's localized to a portion of a graph. But my question is how does the database itself know where those nodes are ? In other words, does it not need to traverse all nodes to find out whether or not they satisfy the query and make decision based on that ?
Neo4j has a special indexing for node labels so that it can find all nodes for a label without searching all nodes. Beyond that you can:
Create your own indexes based on node properties (either schema indexes or legacy indexes) in order to find nodes as starting points
Query by node IDs to find a starting point (though I'd suggest using your own property with an index if you need to identify nodes more permanently)
In general localized searches mean: you start from a smallish set of starting points which can be people, products, places, orders etc.
A portion of the graph that is annotated with a label, often doesn't fall into that category, i.e. all doctors are not a smallish set of starting points.
Your query would probably touch a large portion of the graph if you traverse out from all doctors to their neighborhoods.
A query like this would be a graph local one:
MATCH (:City {name:"SFO"})<-[:RESIDES_IN]-(d:Doctor)-[presc:PRESCRIBES]->(m:Medicine)
RETURN d.name, m.name, sum(presc.amount) as amount
I try to simplify my question. If all nodes in Neo4jDB have same label Science, what's the difference between MATCH n WHERE n.ID="UUID-0001" RETURN n and MATCH (n:Science) WHERE n.ID="UUID-0001" RETURN n. Why the performance is not the same?
My Neo4j database contains about 70000 nodes and 100 relations.
The nodes have two types: Paper and Author, and they both have an ID field.
I created each node with corresponding label, and I also use ID as the index.
However, since one of my functions need to query nodes by ID without considering the label. The query just like: MATCH n WHERE n.ID="UUID-0001" RETURN n. The query time cost about 4000~5000 ms!
But after adding Science for each node and using MATCH (n:Science) WHERE n.ID="UUID-0001" RETURN n. The query time became about 1000~1100 ms. Does anyone know the difference between these two cases?
PS. Count(n:Science) = Count(n:Paper) + Count(n:Author), which mean each node has two labels.
Because for every label Neo4j automatically creates an extra index. The Cypher language can be broadly thought of as piping + filtering, so Match n WHere ... will first get every node and then filter on the where part. Whereas Match (n:Science) Where... will get every node with label science (using an index) and then try to match the where. From your query performance we can see that about 1/5th of your nodes were marked science so the query runs in a fifth he time, because it did a fifth as many comparisons.
Even though I got the advisement from #phil_20686 and #Michael Hunger, but I think these answers do not solve my question.
I think there are some tricks when using label. If their are 10 thousand nodes in Neo4j DB, and the type of these nodes are the same. The query will perform better when adding label to these nodes.
I hope this post can help some people and give me some feedback if you find the reasons. Thanks.