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We present an elementary way to transform an expander graph into a simplicial complex where all high order random walks have a constant spectral gap, i.e., they converge rapidly to the stationary distribution. As an upshot, we obtain new constructions, as well as a natural probabilistic model to sample constant degree high-dimensional expanders. In particular, we show that given an expander graph G, adding self loops to G and taking the tensor product of the modified graph with a high-dimensional expander produces a new high-dimensional expander. Our proof of rapid mixing of high order random walks is based on the decomposable Markov chains framework introduced by [Jerrum et al., 2004].
@InProceedings{liu_et_al:LIPIcs.ITCS.2020.12,
author = {Liu, Siqi and Mohanty, Sidhanth and Yang, Elizabeth},
title = {{High-Dimensional Expanders from Expanders}},
booktitle = {11th Innovations in Theoretical Computer Science Conference (ITCS 2020)},
pages = {12:1--12:32},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-134-4},
ISSN = {1868-8969},
year = {2020},
volume = {151},
editor = {Vidick, Thomas},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ITCS.2020.12},
URN = {urn:nbn:de:0030-drops-116974},
doi = {10.4230/LIPIcs.ITCS.2020.12},
annote = {Keywords: High-Dimensional Expanders, Markov Chains}
}