FIG. 6.

TPR (a, b) and FDR (c, d) of our algorithm implemented on a network of $n=300$ nodes with heterogeneity in both activity distribution and backbone degree, for an observation window of $T=10000$ time steps (a, c) or $T=30000$ time steps (a, c). Both activities and backbone degrees follow power-law distributions with exponents ${\beta }_a$ and ${\beta }_d$, respectively. Other parameters are set to $\lambda =0.9, \mu =0.1$, and $\gamma =0.5$. Each point is an average of ten independent simulations.