Identifying Influential Spreaders Based on Adaptive Weighted Link Model

Identifying influential spreaders in complex networks is crucial in understanding, controlling and accelerating spreading processes for diseases, information, innovations, behaviors, and so on. We proposed a semi-local-information-based algorithm named the adaptive weighted link model (AWLM), which classifies the links in the subgraph made up of the second-order neighbors of nodes and gives them different weights adaptively. The adaptive weighted link model is completely depends on the semi-local topological structure and thus can be calculated not only faster but also under the case where the global topology is not known, especially when the network is sparse, the time complexity is approximate linear. Empirical analyses of the Susceptible-Infected-Recovered (SIR) spreading dynamics on ten real networks show that the adaptive weighted link model always perform the best in comparison with well-known state-of-the-art methods.