Neo4j Graph Data Science Pros and Cons

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 Review collected by and hosted on G2.com.

Neo4j Aura support has limitation of ram and storage and also cost is too high Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

Graph data science python library need to be improved as beginners are might get confused or trapped in the Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Neo4j's performance is generally robust. However, it can sometimes be slower when working with larger datasets. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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! Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Neo4j Graph Data Science for its ability to leverage the inherent structure of graph data for advanced analytics. It provides a range of graph algorithms, seamless integration with Neo4j, scalability for large datasets, an extensive algorithm library, and benefits from collaboration and open-source contributions. Overall, these features make it a powerful tool for analyzing complex interconnected datasets and uncovering valuable insights. Review collected by and hosted on G2.com.

One potential drawback of Neo4j Graph Data Science is its learning curve. For users who are new to graph databases or graph analytics, there can be a significant learning curve associated with understanding the underlying graph concepts and algorithms. This learning process may require extra time and effort to gain proficiency in effectively utilizing the tool. Additionally, as graph analytics can be a specialized field, finding comprehensive and easily accessible learning resources specific to Neo4j Graph Data Science might sometimes pose a challenge. While the tool offers powerful features, its initial learning curve can be a deterrent for users looking for quick and easy adoption or those without prior experience in graph analysis. Review collected by and hosted on G2.com.

GDS stands out as the top choice for performing modeling and advanced analytics natively within the graph environment. Furthermore, each release brings performance updates, functionality improvements to extant algorithms and techniques, as well as additions of new techniques in the toolkit. The ability to perform graph-native analytical tasks through APIs like the python neo4j library facilitates cross-platform/cross-environment work as well. Review collected by and hosted on G2.com.

While I understand GDS is getting updates/added features with each release, optimize/tuning hyperparameters isn't straightforward, it still seems easier to pull everything out of the graph into memory, optimize, then rinse/repeat in the graph, which isn't ideal. Review collected by and hosted on G2.com.

Very easy to onboard technical and less technical staff. Customer support is incredible. Overall really impressed. Review collected by and hosted on G2.com.

Sometimes there's a lack of transparency as to the exact algorithms/mathematical equations used 'under the hood'. Pricing is on the higher end. With the current progress in generative AI (e.g. language and diffusion models), I'd love to see neo4j wake up more to this reality and ensure relevant integrations & models are being offered by GDS soon, otherwise I see a risk of neo4j falling behind. Review collected by and hosted on G2.com.

The wide range of well designed algorithms that cover a broad set of use cases across the wide range of market segments we work in. Review collected by and hosted on G2.com.

The graph model's flexiblity is a strength, but flexibility also means finding the best pattern for a use case can be tricky. Review collected by and hosted on G2.com.

Neo4J data science performs data modeling and data analytics on graphic environments. Neo4J has well designed algorithms can be used in multiple use cases. User interface is easy to navigate and work Review collected by and hosted on G2.com.

nothing I see as of now. It is well organised Review collected by and hosted on G2.com.