Identifying Influential Spreaders of Epidemics on Community Networks

TL;DR

This paper proposes a novel community-aware strategy for identifying influential epidemic spreaders in networks, leveraging k-shell decomposition to improve accuracy and robustness over existing methods.

Contribution

It introduces a new method that separates weak and strong ties in k-shell decomposition to better identify influential spreaders in community networks.

Findings

The strategy outperforms existing methods in empirical social networks.

It remains effective even with network structural errors.

The approach leverages connectivity patterns among neighbors.

Abstract

An efficient strategy for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of weak ties among nodes bridging over communities. A strategy for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this strategy, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition, weak ties and strong ties are processed separately. The strategy was used on empirical networks constructed from online social networks, and results indicated that this strategy is more accurate than other strategies. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the strategy remained robust even when there were errors in the structure of the network.