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Elizabeth Yang

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

2 papers
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2

STOC Conference 2023 Conference Paper

Local and Global Expansion in Random Geometric Graphs

  • Siqi Liu 0005
  • Sidhanth Mohanty
  • Tselil Schramm
  • Elizabeth Yang

Consider a random geometric 2-dimensional simplicial complex X sampled as follows: first, sample n vectors u 1 ,…, u n uniformly at random on S d −1 ; then, for each triple i , j , k ∈ [ n ], add { i , j , k } and all of its subsets to X if and only if ⟨ u i , u j ⟩ ≥ τ, ⟨ u i , u k ⟩ ≥ τ, and ⟨ u j , u k ⟩ ≥ τ. We prove that for every ε > 0, there exists a choice of d = Θ(log n ) and τ = τ(ε, d ) so that with high probability, X is a high-dimensional expander of average degree n ε in which each 1-link has spectral gap bounded away from 1/2.

STOC Conference 2022 Conference Paper

Testing thresholds for high-dimensional sparse random geometric graphs

  • Siqi Liu 0005
  • Sidhanth Mohanty
  • Tselil Schramm
  • Elizabeth Yang

The random geometric graph model GRG d ( n , p ) is a distribution over graphs in which the edges capture a latent geometry. To sample G ∼ GRG d ( n , p ), we identify each of our n vertices with an independently and uniformly sampled vector from the d -dimensional unit sphere S d −1 , and we connect pairs of vertices whose vectors are “sufficiently close,” such that the marginal probability of an edge is p . Because of the underlying geometry, this model is natural for applications in data science and beyond.

v2026.09.13