$\lambda_\infty$ & Maximum Variance Embedding: Measuring and Optimizing Connectivity of A Graph Metric

Bobkov, Houdr\'e, and the last author [2000] introduced a Poincar\'e-type functional parameter, $\lambda_\infty$, of a graph and related it to connectivity of the graph via Cheeger-type inequalities. A work by the second author, Raghavendra, and Vempala [2013] related the complexity of $\lambda_\infty$ to the so-called small-set expansion (SSE) problem and further set forth the desiderata for NP-hardness of this optimization problem. We confirm the conjecture that computing $\lambda_\infty$ is NP-hard for weighted trees. Beyond measuring connectivity in many applications we want to optimize it. This, via convex duality, leads to a problem in machine learning known as the Maximum Variance Embedding (MVE). The output is a function from vertices to a low dim Euclidean space, subject to bounds on Euclidean distances between neighbors. The objective is to maximize output variance. Special cases of MVE into $n$ and $1$ dims lead to absolute algebraic connectivity [1990] and spread constant [1998], that measure connectivity of the graph and its Cartesian $n$-power, respectively. MVE has other applications in measuring diffusion speed and robustness of networks, clustering, and dimension reduction. We show that computing MVE in tree-width dims is NP-hard, while only one additional dim beyond width of a given tree-decomposition makes the problem in P. We show that MVE of a tree in 2 dims defines a non-convex yet benign optimization landscape, i.e., local=global optima. We further develop a linear time combinatorial algorithm for this case. Finally, we denote approximate Maximum Variance Embedding is tractable in significantly lower dims. For trees and general graphs, for which Maximum Variance Embedding cannot be solved in less than $2$ and $\Omega(n)$ dims, we provide $1+\varepsilon$ approximation algorithms for embedding into $1$ and $O(\log n /\varepsilon^2)$ dims, respectively