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Universal Data Analytics as Semantic Spacetime(Part 9)

Part 9: Graph structure, process computation, and dimensional reduction

There’s surely an uncountable number of atoms in a beam holding up your roof, and it’s clear that we don’t need to refer to each atom to talk about the role of the beam. The beam is a superagent; it has its own story, on a much larger scale than the atoms. It relies compositionally on the atoms within it, but they don’t play a direct role in the function of the beam. You could replace the specific atoms with something else, and it would still work. There is a separation of phenomena across scales — effective descriptions dominate the stories we tell. When we shift perspective, from one scale to another, eliminating unwanted degrees of freedom, we both reduce the dimensionality of the problem, and we reorganize computation to achieve a sufficient answer. In the process, we go from something “uncountable” to “a manageable few”. In this post, I want to discuss the foundations for such effective pictures, coarse-graining, and renormalization of models, when computing answers from graphs.


In studying graphs, we’re not just looking for shortest paths, or to how get from one point to another in the quickest time: we may actually want to know where the weakest points in a network are, or find alternative (even hidden) routes, backdoors and, covert channels. We might be looking for the optimum place to establish a new node, in order to serve others, or to depend on a critical source. We might want to turn a timeline of events into a map of knowledge, turning happenings (“X reads a book”) into invariant state (“X knows the book”). Finally, we may look for co-activation clusters to find correlated events, items, or relationships!

Graphs are maps, processes, and calculators all in one. The first machine learning graph was the Internet routing infrastructure: RIP, OSPF, BGP, etc. Today, we have begun associate graphs with machine learning particularly for“AI” (Artificial Intelligence) in a number of ways: the strengths of causal connections can be used to record history and predict priorities; the clusters that form large scale structure can identify patterns of similarity for inference. Graphs and networks are now basic tools of “AI”. In all these cases, how we identify process scales is key to finding the answers we seek.

However, when we look at a huge graph, we are looking at more than just a database — it’s filled with both the remnants of old and harbingers of new processes. We might be interested in querying its large scale patterns, single paths, or topographical features — but this requires iterative interaction with the many alternatives through it. A one-off query statement, such as we’re used to from archival (e.g. SQL) databases, usually won’t cut it. Few other kinds of database can easily show patterns in data in the same way that a graph database can, but that doesn’t mean it’s easy. This whole topic makes graphs part of an unfolding story about knowledge representation that probably begins and ends with