Cryptocurrencies return cross-predictability and technological similarity yield information on risk propaga-tion and market segmentation. To investigate these effects, we build a time-varying network for cryptocur-rencies, based on the evolution of return cross-predictability and technological similarities. We developa dynamic covariate-assisted spectral clustering method to consistently estimate the latent communitystructure of cryptocurrencies network that accounts for both sets of information. We demonstrate thatinvestors can achieve better risk diversification by investing in cryptocurrencies from different communities.A cross-sectional portfolio that implements an inter-crypto momentum trading strategy earns a 1.08% dailyreturn. By dissecting the portfolio returns on behavioral factors, we confirm that our results are not drivenby behavioral mechanisms.