aha, finally a chance to post my only meaningful contribution to computer science: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=72844

aha, finally a chance to post my only meaningful contribution to computer science: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7284430 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=tru... draft here (because I can't give away the final version): https://github.com/BrendanBenshoof/pyVHASH/raw/master/Paper/workshop.pdf https://github.com/BrendanBenshoof/pyVHASH/raw/master/Paper/... Nifty because it is distributed, works in any metric space where voronoi regions make sense (XOR and non-euclidian ones as a start!) and easy to make into an online algorithim that updates as nodes join and exit. basically: given a center point $center$ given a list of candidate points $candidate$ to output a list $peers$ sort $candidates$ by distance from $center$ remove the closest candidate and add to $peers$ (it will always be a delaunay peer) for $c$ in candidates: if any member of $peers$ is closer to $c$ than $center$ then reject $c$ else add $c$ to $peers$ essentially this generates most of a delaunay triangulation, and you can quickly find the voronoi generator of a given point by greedily traversing the resulting graph.