Based on the cryptocurrency market dynamics, this study presents a general methodology for analyzing evolving correlation structures in complex systems using the $q$-dependent detrended cross-correlation coefficient $\rho (q,s)$. By extending traditional metrics, this approach captures correlations at varying fluctuation amplitudes and timescales. The method employs $q$-dependent minimum spanning trees ($q\mathrm{MSTs}$) to visualize evolving network structures. Using minute-by-minute exchange rate data for 140 cryptocurrencies on Binance (January 2021–October 2024), a rolling window analysis reveals significant shifts in $q\mathrm{MSTs}$, notably around April 2022 during the Terra/Luna crash. Initially centralized around Bitcoin (BTC), the network later decentralized, with Ethereum and others gaining prominence. Spectral analysis confirms BTC's declining dominance and increased diversification among assets. A key finding is that medium-scale fluctuations exhibit stronger correlations than large-scale ones, with $q\mathrm{MSTs}$ based on the latter being more decentralized. Properly exploiting such facts may offer the possibility of a more flexible optimal portfolio construction. Distance metrics highlight that major disruptions amplify correlation differences, leading to fully decentralized structures during crashes. These results demonstrate $q\mathrm{MSTs}$' effectiveness in uncovering fluctuation-dependent correlations, with potential applications beyond finance, including biology, social and other complex systems.

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• Clustering
• Community structure
• Degree correlations
• Degree distributions
• Dynamics of networks
• Network structure
• Financial networks