Graph-Based Data Science¶
Cut to the Chase¶
- To get started right away, jump to Getting Started
- For other help, see Community Resources
- For an extensive, hands-on coding tour through kglab, follow the Tutorial notebooks
- Check the source code at https://github.com/DerwenAI/kglab
FAQ: Why build yet another graph library, when there are already so many available?
A short list of primary motivations have been identified for kglab, its design criteria, and engineering trade-offs:
Popular Graph Libraries¶
Point 1: integrate with popular graph libraries, including RDFlib, OWL-RL, pySHACL, NetworkX, iGraph, PyVis, node2vec, pslpython, pgmpy, and so on – several of which would otherwise not have much common ground.
Data Science Workflows¶
Point 2: close integration plus example code for working with the "PyData" stack, namely pandas, NumPy, scikit-learn, matplotlib, etc., as well as PyTorch, and other quintessential data science tools.
Distributed Systems Infrastructure¶
Point 3: integrate efficiently with Big Data tools and practices for contemporary data engineering and cloud computing infrastructure, including: Ray, Jupyter, RAPIDS, Apache Arrow, Apache Parquet, Apache Spark, etc.