Consider an observer operating on a branchial graph
Consider an observer operating on a branchial graph
The branchial is already defining ancestral closeness
Observer coarse-graining a generic graph
Observer coarse-graining a generic graph
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GraphData/@Take[GraphData[40],5]
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GraphData/@Take[GraphData[100],5]
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GraphData/@Take[GraphData[200],UpTo[5]]
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ResourceFunction["HypergraphToGraph"][ResourceFunction["WolframModel"][{{1,2,1},{1,3,4}}->{{4,5,4},{5,4,3},{1,2,5}},{{0,0,0},{0,0,0}},500,"FinalState"]]
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What is coarse-graining on a graph?
What is coarse-graining on a graph?
Connect every node directly to its 2-step as well as 1-step neighbors......
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CycleGraph[10,DirectedEdgesTrue]
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GraphPower
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GraphPower
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Reduction method: “equate two nodes”
Reduction method: “equate two nodes”
Simple case: merging of triangles in triangulation
Simple case: merging of triangles in triangulation
Another form of coarse-graining: backtrack to ancestry; or forward-track to merging
Another form of coarse-graining: backtrack to ancestry; or forward-track to merging
Eventual merging allows coarse-graining to be consistent
Apply graph transformation rules, and say that nodes that evolve into “each other” after n steps are equivalent
Apply graph transformation rules, and say that nodes that evolve into “each other” after n steps are equivalent
Branchial case
Branchial case
Can either coarse grain by by-fiat conflating different paths at a particular time slice...
Can either coarse grain by by-fiat conflating different paths at a particular time slice...
Or by taking time-bins and saying things are equivalent if they evolve into other in a certain time...
Or by taking time-bins and saying things are equivalent if they evolve into other in a certain time...
But to make this meaningful, we need to project to a consistent somewhat-time-independent branchial space
[ Fuzzy logic analog: the observer has a Gaussian kernel in continuous branchial space ]
[ Fuzzy logic analog: the observer has a Gaussian kernel in continuous branchial space ]