Accurate and efficient detection of critical links in network to minimize information loss

We address the problem of efficiently detecting critical links in a large network. Critical links are such links that their deletion exerts substantial effects on the network performance such as the average node reachability. We tackle this problem by proposing a new method which consists of three acceleration techniques: redundant-link skipping (RLS), marginal-node pruning (MNP) and burn-out following (BOF). All of them are designed to avoid unnecessary computation and work both in combination and in isolation. We tested the effectiveness of the proposed method using two real-world large networks and two synthetic large networks. In particular, we showed that the proposed method can estimate the performance degradation by link removal without introducing any approximation within a computation time comparable to that needed by the bottom-k sketch which is a summary of dataset and can efficiently process approximate queries, i.e., reachable nodes, on the original dataset, i.e., the given network. Further, we confirmed that the measures easily composed by the well known existing centralities, e.g. in/out-degree, betweenness, PageRank, authority/hub, are not able to detect critical links. Links detected by these measures do not reduce the average reachability at all, i.e., not critical at all.