  The identification of vital nodes that maintain the network connectivity is a
long-standing challenge in network science. In this paper, we propose a
so-called reverse greedy method where the least important nodes are
preferentially chosen to make the size of the largest component in the
corresponding induced subgraph as small as possible. Accordingly, the nodes
being chosen later are more important in maintaining the connectivity.
Empirical analyses on ten real networks show that the reverse greedy method
performs remarkably better than well-known state-of-the-art methods.
