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Ollivier-Ricci curvature and the spectrum of the normalized graph Laplace operator

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abstract

We prove the following estimate for the spectrum of the normalized Laplace operator $\Delta$ on a finite graph $G$, \begin{equation*}1- (1- k[t])^{\frac{1}{t}}\leq \lambda_1 \leq \cdots \leq \lambda_{N-1}\leq 1+ (1- k[t])^{\frac{1}{t}}, \,\forall \,\,\text{integers}\,\, t\geq 1. \end{equation*} Here $k[t]$ is a lower bound for the Ollivier-Ricci curvature on the neighborhood graph $G[t]$, which was introduced by Bauer-Jost. In particular, when $t=1$ this is Ollivier's estimates $k\leq \lambda_1\leq \ldots \leq \lambda_{N-1}\leq 2-k$. For sufficiently large $t$ we show that, unless $G$ is bipartite, our estimates for $\lambda_1$ and $\lambda_{N-1}$ are always nontrivial and improve Ollivier's estimates for all graphs with $k\leq 0$. By definition neighborhood graphs are weighted graphs which may have loops. To understand the Ollivier-Ricci curvature on neighborhood graphs, we generalize a sharp estimate of the Ricci curvature given by Jost-Liu to weighted graphs with loops and relate it to the relative local frequency of triangles and loops.

fields

math.OC 1

years

2024 1

verdicts

UNVERDICTED 1

representative citing papers

Resistance Distance and Linearized Optimal Transport on Graphs

math.OC · 2024-04-23 · unverdicted · novelty 7.0

Proves that the squared discrete transportation distance between nearby measures on a connected graph is bounded by the quadratic form of a reweighted Laplacian pseudoinverse, yielding a resistance distance with multiple characterizations and showing the random walk as gradient flow on the resulting

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  • Resistance Distance and Linearized Optimal Transport on Graphs math.OC · 2024-04-23 · unverdicted · none · ref 4 · internal anchor

    Proves that the squared discrete transportation distance between nearby measures on a connected graph is bounded by the quadratic form of a reweighted Laplacian pseudoinverse, yielding a resistance distance with multiple characterizations and showing the random walk as gradient flow on the resulting