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This talk explores Graph Thinking as a means for conceptualizing problems which 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.
This talk introduces a set of intuitive examples which convey the power of graph technologies – plus their trade-offs – to domain experts, to be used as a starting point for new graph projects. This approach has been refined through business use cases in industrial AI for firms in EU, and attempts to overcome some of the cognitive hurdles that organizations face during large graph initiatives.
To put this 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, along with a survey of available libraries to leverage for different aspects of graph technologies.
Then we'll show the 'kglab' abstraction layer which integrates these various libraries into the PyData stack
Talk+Live Q&A at the Eastern Auditorium in Connected Data World Center
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