The Chebyshev-distance asymptotic conjecture for hypergraph clique-shadows

For each k3k\geq 3, let Δk\Delta_k be the maximum Chebyshev distance between the coordinatewise powers of the principal eigenvectors of a connected kk-graph and its clique-shadow:

Δk:=maxHH(k)D(y^,x^).\Delta_k:=\max_{H\in\mathcal{H}(k)}D(\hat y,\hat x).

Here D(y^,x^)=maxvyvkxv2D(\hat y,\hat x)=\max_v|y_v^k-x_v^2|, where yy and xx are the principal eigenvectors of HH and (H)\partial^*(H), respectively, and y^=(yvk)vV\hat y=(y_v^k)_{v\in V} and x^=(xv2)vV\hat x=(x_v^2)_{v\in V}. Chebyshev-distance conjecture.

Δk=12+o(1)\Delta_k=\frac12+o(1)

for all k3k\geq 3. The surrounding theorem proves the upper bound D(y^,x^)1/2D(\hat y,\hat x)\leq 1/2 and establishes only the asymptotic lower bound limkΔk=1/2\lim_{k\to\infty}\Delta_k=1/2; the stated uniform asymptotic formula is not resolved in the supplied text.

Sources & referencesView supporting material

Primary source

Gregory J. Clark, Felipe Thomaz and Andrew Stephen, “On the Effect of Data Dimensionality on Eigenvector Centrality”, arXiv:2201.12034 (2022).

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