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Neo4j Graph Data Science Reviews & Product Details

Neo4j Graph Data Science Overview

What is Neo4j Graph Data Science?

Neo4j Graph Data Science is a data science and machine learning engine that uses the relationships in your data to improve predictions. It plugs into enterprise data ecosystems so you can get more data science projects into production quickly. Using a catalog of over 65 pretuned graph algorithms, data scientists can explore billions of data points in seconds to identify hidden connections and generate compelling visualizations that lead to better stakeholder decision making. Practical business applications and operations benefit from the context-first analysis that only graphs can provide across projects like recommendation engines, anomaly and fraud detection, route optimization, marketing, network analysis, and many more.

Neo4j Graph Data Science Details
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Product Description

Neo4j Graph Data Science is an analytics and ML engine that uses the relationships in your data to improve predictions. It plugs into enterprise data ecosystems so you can get more data science projects into production quickly. Using pretuned graph algorithms, data scientists can explore billions of data points in seconds to identify hidden connections and generate compelling visualizations that lead to better stakeholder decision making. Practical business applications and operations benefit from the context-first analysis that only graphs can provide across projects like recommendation engines, anomaly and fraud detection, route optimization, marketing, network analysis, and many more.


Seller Details
Year Founded
2007
HQ Location
San Mateo, CA
Twitter
@neo4j
46,379 Twitter followers
LinkedIn® Page
www.linkedin.com
920 employees on LinkedIn®

NJ
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Recent Neo4j Graph Data Science Reviews

PT
Peter T.Mid-Market (51-1000 emp.)
4.5 out of 5
"Neo4J GDS excellent for performing analysis and modeling on graph data"
GDS stands out as the top choice for performing modeling and advanced analytics natively within the graph environment. Furthermore, each release br...
DK
Dipak K.Mid-Market (51-1000 emp.)
4.5 out of 5
"Let your data speak using neo4j"
Neo4j's Graph data science library is awesome, I have developed 2 recommendation system using their FastRP embedding algorith which finds the embed...
DK
Dipak K.Mid-Market (51-1000 emp.)
5.0 out of 5
"Best for data science problems which are related to Graphs or finding pattern from your data"
i really liked the support for popular machine learning problem statement such as community detection, similarity finding between neighbour nodes w...

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16 Neo4j Graph Data Science Reviews

4.5 out of 5
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16 Neo4j Graph Data Science Reviews
4.5 out of 5
16 Neo4j Graph Data Science Reviews
4.5 out of 5

Neo4j Graph Data Science Pros and Cons

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Pros and Cons are compiled from review feedback and grouped into themes to provide an easy-to-understand summary of user reviews.
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Overall Review Sentiment for Neo4j Graph Data ScienceQuestion

Time to Implement
<1 day
>12 months
Return on Investment
<6 months
48+ months
Ease of Setup
0 (Difficult)
10 (Easy)
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DK
AI/ML Engineer
Mid-Market(51-1000 emp.)
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Review source: G2 invite
Incentivized Review
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 Review collected by and hosted on G2.com.

What do you dislike about Neo4j Graph Data Science?

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

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. Review collected by and hosted on G2.com.

DK
Software Engineer
Information Technology and Services
Mid-Market(51-1000 emp.)
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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 Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

GM
Small-Business(50 or fewer emp.)
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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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

SU
Small-Business(50 or fewer emp.)
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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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Verified User in Marketing and Advertising
IM
Small-Business(50 or fewer emp.)
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Review source: Seller invite
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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! Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

SG
Data Engineer
Mid-Market(51-1000 emp.)
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What do you like best about Neo4j Graph Data Science?

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.

What do you dislike about Neo4j Graph Data Science?

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.

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

We were attempting to track all the intermediate steps involved in the production and consumption of finished goods within the context of traceability processes in the consumer industry. Traceability refers to tracking and tracing products along the supply chain, from raw materials to the end consumer. Review collected by and hosted on G2.com.

PT
Mid-Market(51-1000 emp.)
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Review source: Seller invite
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What do you like best about Neo4j Graph Data Science?

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.

What do you dislike about Neo4j Graph Data Science?

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.

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

Graph Data Science can solve various problems across use cases, which is of great benefit when working with stakeholders across industries and contexts. It provides a substantial toolkit to reach solutions natively within the graph, which, coupled with the underlying database technology performance, meaningfully speeds up iterative experimentation and analytics/modeling processes. Review collected by and hosted on G2.com.

PL
Small-Business(50 or fewer emp.)
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What do you like best about Neo4j Graph Data Science?

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

What do you dislike about Neo4j Graph Data Science?

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.

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

We use Neo4j Graph Data Science in both internal Deep Learning/R&D projects as well as in commercial customer/product pipelines. Review collected by and hosted on G2.com.

MM
Mid-Market(51-1000 emp.)
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What do you like best about Neo4j Graph Data Science?

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.

What do you dislike about Neo4j Graph Data Science?

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.

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

We typically apply GDS to modeling and generating analytics and insight for complex physical systems. We need to develop a 'digital twin' in these cases, for which the graph model and the GDS paradigm are particularly well-suited. Review collected by and hosted on G2.com.

LT
Technology Lead
Enterprise(> 1000 emp.)
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What do you like best about Neo4j Graph Data Science?

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.

What do you dislike about Neo4j Graph Data Science?

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

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

It solves complex data analysis which provides the analytics in the form of graphs in a very less amount of time. Review collected by and hosted on G2.com.