Finding influential spreaders from human activity beyond network location

TL;DR

This paper proposes a method to identify influential spreaders in social networks without full network data by using social interaction surveys, highlighting the importance of individual behavior and community bridging in spreading dynamics.

Contribution

It introduces a survey-based approach to find influential spreaders, emphasizing social mechanisms over complete network topology for practical applications.

Findings

Probabilistic connection to hubs predicts influential spreaders.

Connecting different communities increases influence in modular networks.

Behavioral data can replace full network information for spreading strategies.

Abstract

Most centralities proposed for identifying influential spreaders on social networks to either spread a message or to stop an epidemic require the full topological information of the network on which spreading occurs. In practice, however, collecting all connections between agents in social networks can be hardly achieved. As a result, such metrics could be difficult to apply to real social networks. Consequently, a new approach for identifying influential people without the explicit network information is demanded in order to provide an efficient immunization or spreading strategy, in a practical sense. In this study, we seek a possible way for finding influential spreaders by using the social mechanisms of how social connections are formed in real networks. We find that a reliable immunization scheme can be achieved by asking people how they interact with each other. From these surveys we find that the probabilistic tendency to connect to a hub has the strongest predictive power for influential spreaders among tested social mechanisms. Our observation also suggests that people who connect different communities is more likely to be an influential spreader when a network has a strong modular structure. Our finding implies that not only the effect of network location but also the behavior of individuals is important to design optimal immunization or spreading schemes.