An Accurate and Efficient Method to Detect Critical Links to Maintain Information Flow in Network

Abstract

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 one existing and two new 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 new method can compute the performance degradation by link removal without introducing any approximation within a comparable computation time 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.