Graph theory, graph convolutional networks, knowledge graphs
This post is a good example of the initial realization that folks experience with graphs as data structures. I can see author exclaiming, "Graphs are an intuitive way to express knowledge!" Graphs as data structures can be more nuanced than is usually discussed.<p>Hypernode graphs seem to be very applicable to knowledge graphs, though they have different names depending on the discipline -- nested state machines, statecharts [1], UML hierarchically nested states [2].<p>I recently participated in a lecture series on Applied Category Theory [3] that touched on the underlying math of composition. The course focused on optimization of design choices in complex decision spaces.<p>[1] <a href="https://statecharts.github.io/" rel="nofollow">https://statecharts.github.io/</a><p>[2] <a href="https://en.wikipedia.org/wiki/UML_state_machine#Hierarchically_nested_states" rel="nofollow">https://en.wikipedia.org/wiki/UML_state_machine#Hierarchical...</a><p>[3] <a href="https://applied-compositional-thinking.engineering/lectures/" rel="nofollow">https://applied-compositional-thinking.engineering/lectures/</a>