English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

It is widely acknowledged that the initial spreaders play an important rolefor the wide spreading of information in complex networks. Thus, a variety ofcentrality-based methods have been proposed to identify the most influentialspreaders. However, most of the existing studies have overlooked the fact thatin real social networks it is more costly and difficult to convince influentialindividuals to act as initial spreaders, resulting in a high risk in maximizingthe spreading. In this paper, we address this problem on the basis of theassumption that large-degree nodes are activated with a higher risk thansmall-degree nodes. We aim to identify the effective initial spreaders tomaximize spreading when considering both the activation risk and the outbreaksize of initial spreaders. On random networks, the analytical analysis revealsthat the degree of optimal initial spreaders does not correspond to the largestdegree of nodes in the network but rather be determined by infectionprobability and difference of activation risk among nodes with differentdegree. Here, we propose a risk-aware metric to identify the effectivespreaders on real networks. The numerical simulation shows that the risk-awaremetric outperforms the existing benchmark centralities in maximizing theeffective spreading.

Maximizing spreading in complex networks with risk in node activation