Slides available here
Python has excellent libraries for working with graphs which provide: semantic technologies, graph queries, interactive visualizations, graph algorithms, probabilistic graph inference, as well as embedding and other integrations with deep learning.
However, almost none of these have integration paths other than writing lots of custom code, and most do not share common file formats. Moreover, few of these libraries integrate effectively with popular data science tools (e.g., pandas, scikit-learn, PyTorch, spaCy, etc.) or with popular infrastructure for scale-out (Apache Spark, Ray, RAPIDS, Apache Parquet, fsspec, etc.) on cloud computing.
This workshop uses kglab – an open source project that integrates RDFlib, OWL-RL, pySHACL, NetworkX, iGraph, pslpython, node2vec, PyVis, and more – to show how to use a wide range of graph-based approaches, blending smoothly into data science workflows, and working efficiently with popular data engineering practices.
The material emphasizes hands-on coding examples which you can reuse; best practices for integrating and leveraging other useful libraries; history and bibliography (e.g., links to primary sources); accessible, detailed API documentation; a detailed glossary of terminology; plus links to many helpful resources, such as online "playgrounds".
Meanwhile, overall we keep a practical focus on use cases.
Additionally, if you've completed Algebra 2 in secondary school and have some business experience working with data analytics – both can come in handy.
See the installation instructions at https://derwen.ai/docs/kgl/tutorial/#installation