  This paper identifies the cryptocurrency market crashes and analyses its
dynamics using the complex network. We identify three distinct crashes during
2017-20, and the analysis is carried out by dividing the time series into
pre-crash, crash, and post-crash periods. Partial correlation based complex
network analysis is carried out to study the crashes. Degree density
($\rho_D$), average path length ($\bar{l}$), and average clustering coefficient
($\overline{cc}$) are estimated from these networks. We find that both $\rho_D$
and $\overline{cc}$ are smallest during the pre-crash period, and spike during
the crash suggesting the network is dense during a crash. Although $\rho_D$ and
$\overline{cc}$ decrease in the post-crash period, they remain higher than
pre-crash levels for the 2017-18 and 2018-19 crashes suggesting a market
attempt to return to normalcy. We get $\bar{l}$ is minimal during the crash
period, suggesting a rapid flow of information. A dense network and rapid
information flow suggest that during a crash uninformed synchronized panic
sell-off happens. However, during the 2019-20 crash, the values of $\rho_D$,
$\overline{cc}$, and $\bar{l}$ did not vary significantly, indicating minimal
change in dynamics compared to other crashes. The findings of this study may
guide investors in making decisions during market crashes.
