Graph Thinking provides a conceptual framework for solving complex analytics problems with graph technologies. This talk explores this conceptual approach, along with popular open source libraries in Python that provide graph technologies, and the
kglab integration project which ties them into the PyData stack. We'll also look at common use cases in industry for graph data science.
This talk explores graph thinking as a way to conceptualize problems that can be solved using graph technologies. Parallels can be found in learning theory, for example how people organize knowledge into graph-like cognitive structures as they progress from novice to practitioner to expert levels in a given field. We'll show a survey of popular open source libraries in Python for different aspects of graph technologies, along with the
kglab abstraction layer that integrates these into the PyData stack. To put this all into context, we'll review a set of common use cases in industry and how graph data science practices can be built using Python open source.