Neo4j Graph Data Science
Reviews
Star rating
Rank in Machine Learning
Pros and cons
Pros
- Ease of Use
- Model Variety
- Problem Solving
- Machine Learning
- Scalability
- Analytics
Cons
- Learning Curve
- Learning Difficulty
- Expensive
- Beginner Difficulty
- Difficult Learning
- Limited Features
Traffic & LLM citations
Source: G2 review data
Winning from
Key stats
Adoption by company size
| Segment | Share of reviews |
|---|---|
| Small Business | 37.5% |
| Mid-Market | 43.8% |
| Enterprise | 18.8% |
Quarterly history
| Quarter | Reviews | Star rating |
|---|
Satisfaction ratings
What reviewers say
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 embeddings of each node and its relationship. It is really fast as every node is connected with each other and hence able to generate embe”
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 which really helps in a user similarity based recommendation, even though sometimes i dont get the exact solution from the documentati”
Neo4j Graph Data Science Review
“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 collaborat”