Random walks on graphs are an essential primitive for many randomised algorithms and stochastic processes. It is natural to ask how much can be gained by running $k$ multiple random walks independently and in parallel. Although the cover time of multiple walks has been investigated for many natural networks, the problem of finding a general characterisation of multiple cover times for $\textit{worst-case}$ start vertices (posed by Alon, Avin, Kouck\'y, Kozma, Lotker, and Tuttle in 2008) remains an open problem. First, we improve and tighten various bounds on the $\textit{stationary}$ cover time when $k$ random walks start from vertices sampled from the stationary distribution. For example, we prove an unconditional lower bound of $\Omega((n/k) \log n)$ on the stationary cover time, holding for any $n$-vertex graph $G$ and any $1 \leq k =o(n\log n )$. Secondly, we establish the $\textit{stationary}$ cover times of multiple walks on several fundamental networks up to constant factors. Thirdly, we present a framework characterising $\textit{worst-case}$ cover times in terms of $\textit{stationary}$ cover times and a novel, relaxed notion of mixing time for multiple walks called the $\textit{partial mixing time}$. Roughly speaking, the partial mixing time only requires a specific portion of all random walks to be mixed. Using these new concepts, we can establish (or recover) the $\textit{worst-case}$ cover times for many networks including expanders, preferential attachment graphs, grids, binary trees and hypercubes.
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