  When the scale of communication networks has been growing rapidly in the past
decades, it becomes a critical challenge to extract fast and accurate
estimation of key state parameters of network links, e.g., transmission delays
and dropped packet rates, because such monitoring operations are usually
time-consuming. Based on the sparse recovery technique reported in [Wang et al.
(2015) IEEE Trans. Information Theory, 61(2):1028--1044], which can infer link
delays from a limited number of measurements using compressed sensing, we
particularly extend to networks with dynamic changes including link insertion
and deletion. Moreover, we propose a more efficient algorithm with a better
theoretical upper bound. The experimental result also demonstrates that our
algorithm outperforms the previous work in running time while maintaining a
similar recovery performance, which shows its capability to cope with
large-scale networks.
