Review: Reconstructing the Geometry of Random Geometric Graphs
math
ML
differential-geometry
paper-review
Geometric models for random graphs have demonstrable utility in modelling real-world networks. One of the quintessential questions in this field is that of reconstructing the geometry of the underlying space from which the graph is sampled. This article reviews the paper by Huang, H., Jiradilok, P. and Mossel, E. on “Reconstructing the geometry of random geometric graphs” which answers in the affirmative.
Locally Linear Embeddings
ML
tutorial
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