The spreading ability of nodes towards localized targets in complex networks

TL;DR

This paper introduces a reversed local path algorithm to evaluate node spreading ability towards specific localized targets in complex networks, improving targeted influence identification and reducing non-target infections.

Contribution

The paper proposes a novel reversed local path algorithm specifically designed for assessing node influence towards localized targets, outperforming existing methods.

Findings

The proposed method outperforms existing algorithms in identifying influential nodes for localized targets.

Influential spreaders identified by the method effectively avoid infecting non-target nodes.

Simulation results validate the effectiveness of the reversed local path algorithm.

Abstract

As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and medicine science the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.