Neo4j Graph Data Science

What do you like best about Neo4j Graph Data Science?

Neo4j's Graph data science library is awesome, I have developed 2 recommendation system using their FastRP embedding algorith which finds the embeddings of each node and its relationship. It is really fast as every node is connected with each other and hence able to generate embeddings based on what your data speaks

What do you dislike about Neo4j Graph Data Science?

Neo4j Aura support has limitation of ram and storage and also cost is too high

What problems is Neo4j Graph Data Science solving and how is that benefiting you?

We have delivered a product recommendation system using neo4j graph data science library such as Fast Random Projection embedding which was helpful in finding the node structure in terms of embeddings, then using its another function KNN algorithm of Graph data science library we were able to find out most similar user and based on that we were able to give user similarity product recommendation.

What do you like best about Neo4j Graph Data Science?

i really liked the support for popular machine learning problem statement such as community detection, similarity finding between neighbour nodes which really helps in a user similarity based recommendation, even though sometimes i dont get the exact solution from the documentation its community forum helps a lot with a instant resolution

What do you dislike about Neo4j Graph Data Science?

Graph data science python library need to be improved as beginners are might get confused or trapped in the

What problems is Neo4j Graph Data Science solving and how is that benefiting you?

so currently i am solving a problem related to data insights and recommendation system and there are 2 most popular techniques are used worldwide i.e. content based recommendation and collaborative filtering, neo4j data science library has feature of finding k nearest neighbor which helps to develop collaborative filtering based recommendation and also neo4j has the capability of storing vector embedding which helps in content based recommendation.

What do you like best about Neo4j Graph Data Science?

What I appreciate most about Neo4j Graph Data Science is its ability to analyse and interpret complex relationships and patterns in data efficiently. GDS provides a comprehensive platform for both real-time and batch analysis. Additionally, its intuitive graph query language, Cypher, and visualisation tools facilitate more insightful exploration and understanding of the corresponding data. This has significantly improved the efficiency and depth of my data-driven projects.

What do you dislike about Neo4j Graph Data Science?

Neo4j's performance is generally robust. However, it can sometimes be slower when working with larger datasets.

What problems is Neo4j Graph Data Science solving and how is that benefiting you?

Neo4j Graph Data Science is helping us solve a range of complex problems central to our work, particularly around protein function annotation. By leveraging the graph database structure, we can model and explore intricate protein interaction networks intuitively and highly flexibly.

The built-in algorithms, including community detection, topological link prediction and node embeddings, have greatly benefited our data analysis. They have enabled us to answer more complex questions and provide insights at a more granular level. For example, node embeddings have proved incredibly useful in creating deep-learning models for detailed protein function annotation

What do you like best about Neo4j Graph Data Science?

Neo4j GDS has been invaluable to our journey here at Basecamp Research. We could leverage the algorithms offered to build useful customer search pipelines. This process would have taken a lot longer and had more risk built into it if we were to attempt to construct the same infrastructure manually around our data.

What do you dislike about Neo4j Graph Data Science?

One of the disadvantages of Neo4J's GDS offering is the upgrading system which involves swapping out jar files in a plugins folder and restarting the instance. This process can cause issues. Also, in some cases, the algorithms we wanted to use were unavailable.

What problems is Neo4j Graph Data Science solving and how is that benefiting you?

Neo4J GDS allows us to structure our protein data in such a way that we can leverage the relationships between individual molecules as well as with other key metadata to predict potential function.

What do you like best about Neo4j Graph Data Science?

I have been working with Neo4j Graph Data Sience for many years, and i would recomend everyone interested in GDS and Machine learning to chech this out. Regardless if you are a beginner and want to learn more, you can start your journey here, or if you are an experienced data scientist and want to scale up or take a different approach to your challenges. They have an extencive library to quickly get started, and the scalability is endless. Highly recomended!

What do you dislike about Neo4j Graph Data Science?

For advanced users, i have a hard time seeing limitations. However, in some ways this is a shortcut for beginners. It is important to learn the basics while beginning your journey, so you understand what is happening "behind the scenes". Neo4j Graph Academy is one way of doing this.

What problems is Neo4j Graph Data Science solving and how is that benefiting you?

It has the power to run our extencive algorithms in a timely and efficient manner, while simultaniously providing tools for exploration and continious development for our offerings.