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arxiv: 1412.3195 · v1 · pith:O4F7JBHTnew · submitted 2014-12-10 · 🧮 math.CO · cs.DM

A Linear Cheeger Inequality using Eigenvector Norms

classification 🧮 math.CO cs.DM
keywords lambdacheegerinequalityconstanteigenvectorfactorlinearquadratic
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The Cheeger constant, $h_G$, is a measure of expansion within a graph. The classical Cheeger Inequality states: $\lambda_{1}/2 \le h_G \le \sqrt{2 \lambda_{1}}$ where $\lambda_1$ is the first nontrivial eigenvalue of the normalized Laplacian matrix. Hence, $h_G$ is tightly controlled by $\lambda_1$ to within a quadratic factor. We give an alternative Cheeger Inequality where we consider the $\infty$-norm of the corresponding eigenvector in addition to $\lambda_1$. This inequality controls $h_G$ to within a linear factor of $\lambda_1$ thereby providing an improvement to the previous quadratic bounds. An additional advantage of our result is that while the original Cheeger constant makes it clear that $h_G \to 0$ as $\lambda_1 \to 0$, our result shows that $h_G \to 1/2$ as $\lambda_1 \to 1$.

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