High Dimensional Expanders: Random Walks, Pseudorandomness, and Unique Games
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Higher order random walks (HD-walks) on high dimensional expanders have played a crucial role in a number of recent breakthroughs in theoretical computer science, perhaps most famously in the recent resolution of the Mihail-Vazirani conjecture (Anari et al. STOC 2019), which focuses on HD-walks on one-sided local-spectral expanders. In this work we study the spectral structure of walks on the stronger two-sided variant, which capture wide generalizations of important objects like the Johnson and Grassmann graphs. We prove that the spectra of these walks are tightly concentrated in a small number of strips, each of which corresponds combinatorially to a level in the underlying complex. Moreover, the eigenvalues corresponding to these strips decay exponentially with a measure we term the depth of the walk. Using this spectral machinery, we characterize the edge-expansion of small sets based upon the interplay of their local combinatorial structure and the global decay of the walk's eigenvalues across strips. Variants of this result for the special cases of the Johnson and Grassmann graphs were recently crucial both for the resolution of the 2-2 Games Conjecture (Khot et al. FOCS 2018), and for efficient algorithms for affine unique games over the Johnson graphs (Bafna et al. Arxiv 2020). For the complete complex, our characterization admits a low-degree Sum of Squares proof. Building on the work of Bafna et al., we provide the first polynomial time algorithm for affine unique games over the Johnson scheme. The soundness and runtime of our algorithm depend upon the number of strips with large eigenvalues, a measure we call High-Dimensional Threshold Rank that calls back to the seminal work of Barak, Raghavendra, and Steurer (FOCS 2011) on unique games and threshold rank.
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