# Identifying vital nodes based on reverse greedy method

## TL;DR

This paper introduces a reverse greedy method for identifying vital nodes in networks, which outperforms existing methods in maintaining network connectivity, by iteratively removing the least important nodes.

## Contribution

The paper proposes a novel reverse greedy approach for vital node identification that demonstrates superior performance over existing methods.

## Key findings

- Reverse greedy method outperforms state-of-the-art techniques
- Method effectively identifies nodes critical for network connectivity
- Empirical results on ten real networks validate the approach

## Abstract

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.