We study random geometric graphs in the high-dimensional setting, where the dimension grows to infinity with the number of vertices. Our central question is: given such a graph, when is it possible to identify the underlying geometry? As the dimension grows relative to the number of vertices, the edges in the graph become increasingly independent, and the underlying geometry becomes less apparent. In this talk I will present recent progress on this question: we show that in sparse geometric random graphs on the unit sphere, if the dimension is at least polylogarithmic in the number of vertices, then the graphs are statistically indistinguishable from Erdos-Renyi graphs, and the underlying geometry disappears. Based on joint work with Siqi Liu, Sidhanth Mohanty, and Elizabeth Yang.
Apr 11, 2022, 4:30 pm – 5:30 pm