Neo4j Graph Data Science vs. Crab
A side-by-side comparison based on G2 review data.
| Metric | Neo4j Graph Data Science | Crab |
|---|---|---|
| Overall rating | 4.5 | 4.5 |
| Review count | 16 | 10 |
| Ease of use | 8.7 | 8.9 |
| Ease of setup | 8.2 | 9.0 |
| Quality of support | 9.0 | 9.4 |
| Meets requirements | 9.1 | 10.0 |
| NPS | 73.333333 | 100 |
| Small Business | 37.5% | 50.0% |
| Mid-Market | 43.8% | 40.0% |
| Enterprise | 18.8% | 10.0% |
| Total page views | 90 | 38 |
| Total LLM citations | 557 | 28 |
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.
- Ease of Use
- Model Variety
- Problem Solving
- Machine Learning
- Scalability
- Analytics
Crab
Crab as known as scikits.recommender is a Python framework for building recommender engines that integrate with the world of scientific Python packages (numpy, scipy, matplotlib), provide a rich set of components from which user can construct a customized recommender system from a set of algorithms and be usable in various contexts: ** science and engineering ** .