Suppose I have 2 sentences that I have created using the below code WITH split(tolower("His dog eats turkey on Tuesday")," ") as text Unwind range(0,size(text)-2) AS i MERGE (w1:Word {name: text[i]}) MERGE (w2:Word {name: text[i+1]}) MERGE (w1)-[:NEXT]->(w2) RETURN w1, w2 WITH split(tolower("My cat eats fish on Saturdays")," ") as text Unwind range(0,size(text)-2) as i MERGE (w1:Word {name: text[i]}) MERGE (w2:Word {name: text[i+1]}) MERGE (w1)-[:NEXT]->(w2) RETURN w1, w2 The Graph looks like something. Now given this what would be a good …

I'm currently working on a problem relating to Discord servers (sort of like a group chat within a social media platform), where I have a program recursively joining servers, looking for invites, and then joining those invites. As the amount of discovered servers grows, I've noticed that the amount of new servers discovered from searching old servers has decreased. If I pick a server to search randomly, for each invite I find in that server, there's a pretty low probability …

Assume that in an application, the user gives us a graph and we want to consider it as a pattern and find all occurrences of the pattern in a graph database (like neo4j). If we knew what the pattern is, we could write the pattern as a static query and run it against our database. However, now we do not know what the pattern is beforehand and receive it from the user in the form of a graph. How can …

When I'm reading about building recommendation systems with collaborative filtering and they generally don't talk about graph databases like neo4j. Are graph databases enough to implement the best recommendation systems? It seems like they can implement realtime recommendations easier than the methods mentioned here: https://eugeneyan.com/writing/real-time-recommendations/ Any weaknesses I should be aware of?

I want to build a recommender system for shops, where I recommend items. I've learned about these systems like with content-based, collaborative filtering and so on. But now I want to make one on a very huge data. Here I talk about a recommender system for shops where I want to recommend some items to shops, based on their similarities with other shops. To have an idea, I speak about 3000 shops, and 1 000 000+ items (I can take …

I work in a bank and most data is in tabular format in relational databases. I have been reading about graph algorithms (page rank), graph libraries (spark graphx) and graph databases (neo4j). I would like to pick a use case from my field (finance). What use case would be suitable for graph algirithms and databases?

I have a neo4j database that stores forum posts. Replies to specific posts are encoded using a replyto relationship. I am trying to retrieve all the information from the database, convert the relationships into node properties, then output all the nodes with the new replyto property in JSON format. I think the replyto property in the JSON object should be an array of all the post IDs that are replies to the given post, but please suggest something better if …

I want to build a Recommendation System to recommend products to users. This is for research purposes. The context-system the engine will be integrated in is also not build yet. So right now I am starting the project, building kind of a E-Commerce & Social-Network-Platform for research purposes. To realize the recommendation system, I want to integrate Neo4j and Elasticsearch with each other. For the content based filtering part Elasticsearch should do its job nicely. For the collaborative filtering part …

Is there a way or tools available to generate or retrieve cypher query from a Neo4j database ? Should we need to store cypher quries along with the graph data for regeneration ?

Basically I'm developing a recommendation system using a graph database (specifically neo4j), and I want to apply recommendation algorithms. Since i'm using a graph database, I can see the recommendation problem as a graph problem, and intuitively i can use graph based algorithms for the recommendation system. From my research, recommendation systems are a subclass of information filtering system that seek to predict the "rating" or "preference" that a user would give to an item. And there exists basically two …

I would like to know if neo4j can be considered a graph processing platform, even though i know that: neo4j: is a graph database management system developed by Neo Technology, Inc. Described by its developers as an ACID-compliant transactional database with native graph storage and processing. graph processing platform: is a platform used for processing graphs, by applying global algorithms towards large graphs (used more in OLAP scenario). Based on this paper http://www.ds.ewi.tudelft.nl/~iosup/perf-eval-graph-proc14ipdps.pdf, neo4j is benchmarked as a processing platform. …

What are the advantages and disadvantages of using Collaborative filtering based recommendation using machine learning approach and graph based approach ? Say I have user purchase data (user_name, user_location, user_company_name, product_name, product_price, product_ingredients) and would like to recommend product for user based on what other user from the same location, company are buying, based on product price, ingredients etc. How to decide on which of them is suitable for a given use case? I would like to evaluate Neo4j (Graph …

Background: Following is from the book Graph Databases, which covers a performance test mentioned in the book Neo4j in Action: Relationships in a graph naturally form paths. Querying, or traversing, the graph involves following paths. Because of the fundamentally path-oriented nature of the datamodel, the majority of path-based graph database operations are highly aligned with the way in which the data is laid out, making them extremely efficient. In their book Neo4j in Action, Partner and Vukotic perform an experiment …

I have used Neo4J to implement a content recommendation engine. I like Cypher, and find graph databases to be intuitive. Looking at scaling to a larger data set, I am not confident No4J + Cypher will be performant. Spark has the GraphX project, which I have not used in the past. Has anybody switched from Neo4J to Spark GraphX? Do the use cases overlap, aside from scalability? Or, does GraphX address a completely different problem set than Neo4J?

I have an excel file containing lot of columns. I want to create a graph database in Neo4J with all these columns as a separate node so that I can establish relationship between them and play around with the cypher query to get to know my data quite well. I did write a cypher query to make each column as a node, but when I run the relationship query it shows me the name of one node and gives a …